Abstract:
Keywords: geopolitical risk, bank lending, credit risk, international spillovers
JEL-Codes: F34, F36, G21
Geopolitical risk has escalated in recent years, fueled by events such as Russia's invasion of Ukraine, rising tensions between China and the West, and conflicts in the Middle East. The potentially adverse economic consequences of heightened geopolitical risk have become a top concern for policymakers and businesses.1 Yet the academic literature on this subject remains nascent. In particular, the financial and international mechanisms through which geopolitical risk affects economies are not well understood. This paper addresses this gap by analyzing how global banks respond to rising geopolitical risk and the resulting spillover effects. Operating across multiple jurisdictions, global banks are inherently exposed to a range of geopolitical shocks, which, unlike other forms of economic risks, often entail uniquely catastrophic outcomes, such as expropriation. At the same time, global banks' credit supply decisions have material effects on firm investment and employment (see, e.g., [Peek andRosengren, 2000,Khwaja and Mian, 2008,Schnabl, 2012,Kalemli-Özcan et al., 2013,Huber, 2018]). Given their global reach, these banks can serve as critical conduits for the propagation of geopolitical risk, including to countries not directly involved in conflict.
In this paper, we investigate how U.S. global banks manage geopolitical risk arising from exposure through their foreign operations, and how this behavior spills over to domestic credit supply. We do so by leveraging multiple sources of confidential supervisory data and both established and newly constructed geopolitical risk indices at the country and bank levels. We begin by establishing three key findings on how geopolitical risk shapes banks' foreign operations. First, geopolitical risk in countries where banks operate increases the credit risk of those banks' directly exposed loans and their overall balance sheets. Second, despite heightened credit risk, banks continue lending to countries with elevated geopolitical risk through their foreign affiliates, even as they reduce cross-border lending to those markets. In other words, they maintain credit access through local operations while retreating from direct cross-border operations.2 Third, this asymmetric response is specific to geopolitical risk, as banks do not adjust foreign operations in the same way to more traditional forms of country risk, such as macroeconomic or sovereign risk.
We show that these findings can be explained through a stylized model in which differences in funding structures between cross-border and local operations shape banks' credit allocation under geopolitical risk. In cross-border lending, banks raise funds domestically, leaving the parent fully liable in the event of loss. By contrast, local operations are at least partly funded through foreign deposits, which do not need to be repaid in the event of expropriation. Based on historical precedent, geopolitical risk heightens the likelihood of expropriation. Governments involved in geopolitical conflicts have, at times, seized foreign assets and extinguished corresponding local liabilities. Even when outright expropriation does not occur, incremental forms of government intervention--such as limits on profit repatriation or imposition of capital controls--can have similar effects. By funding foreign affiliates locally, banks reduce the volume of profits or principal that must be extracted across borders, thereby lowering their exposure to such intervention. As a result, banks with affiliate-based lending can partially offset asset losses through a reduction in liabilities, lowering the net loss from geopolitical risk. This asymmetry in expected losses creates a wedge in returns across modes of foreign operation, leading banks to reduce cross-border lending while maintaining local operations under geopolitical risk.3
Beyond shaping banks' foreign operations, geopolitical risk abroad also generates spillover effects on domestic credit supply through capital requirements applied at the consolidated level, as predicted by the model and confirmed by our empirical analysis. We find that U.S. global banks reduce commercial and industrial (C&I) lending to domestic firms when geopolitical risk abroad rises. This effect is most pronounced when the risk originates in countries where the banks operate through local affiliates, underscoring how the structure of foreign operations--that is, local affiliate versus cross-border lending--shapes the transmission of geopolitical shocks. Our findings highlight the role internationally active banks play as conduits through which geopolitical instability spills over into domestic credit markets and, hence, the broader economy.
We begin the analysis by compiling and constructing indices for country-specific geopolitical risk (CGPR) and bank-specific geopolitical risk (BGPR). For the former, we draw Caldara and Iacoviello, 2022's index for 44 countries, which is based on a count of mentions of war and related terms in newspaper articles. To complement this measure, we construct a new CGPR index by applying textual analysis with similar terms to firms' earnings-call transcripts, following the methodology outlined in [Hassan et al., 2019,Hassan et al., 2023]. The earnings-call-based index enables us to focus on the geopolitical risks most salient to firms' perception and to distinguish between country-specific geopolitical risk arising from acts versus threats. Together, these measures provide a more comprehensive picture of country-specific geopolitical risk and enable us to assess the robustness of our findings across distinct sources of information. Compared with broader macro-level risk indicators--such as [Hassan et al., 2019,Hassan et al., 2023]'s country risk index (CRI) and Ahir et al., 2022's World Uncertainty Index--both the CGPR and BGPR indices reveal distinct patterns reflecting the realization and salience of geopolitical events and risks.
Equipped with the CGPR indices, we construct BGPR indices that capture individual banks' exposure to CGPR through their foreign operations. Specifically, we calculate BGPR by weighting each bank's share of assets in a country by that country's CGPR index, then summing across all foreign countries (excluding the United States). Data on banks' foreign exposures are derived from confidential FFIEC 009 reports submitted to the Federal Reserve. U.S. banks have substantial exposure to a wide range of countries, with significant variation across countries and over time within each bank. As a result, BGPR varies across banks and over time, providing the identifying variation we exploit to estimate the effects of geopolitical risk on bank behavior.
Using the indices, we first examine the effects of geopolitical risk on banks' credit risk, using data from FR Y-14Q reports, which provide loan-level information on the amount and terms of C&I lending by all banks participating in Federal Reserve stress tests. Based on regressions at the bank-country-time level, we find that the probability of default on loans to a country--as assigned by the banks--increases with rising geopolitical risk in that country. To validate this result, we conduct an event study of two major geopolitical shocks: the Crimea conflict in 2013:Q4 and the Russia-Ukraine war in 2022:Q1. Consistent with our regression findings, we show that the sharp increase in geopolitical risk in Russia following these events led to a significantly greater rise in the default probabilities of loans to Russian borrowers relative to loans to borrowers from other countries. Building on these results, we assess whether the increase in credit risk is large enough to materially affect banks' overall loan portfolios. Our bank-level analysis shows that as exposure to foreign geopolitical risk rises, the aggregate probability of default in U.S. banks' loan portfolios increases significantly. In other words, foreign geopolitical risk shocks materially elevate the overall credit risk that U.S. banks face.
Next, we investigate how banks adjust their foreign lending in response to rising credit risk, using FFIEC 009 data on banks' foreign claims by country. We find that U.S. banks' responses vary systematically by the mode of foreign operation. Regressions at the bank-country level show that banks reduce their cross-border lending to countries experiencing elevated geopolitical risk, while lending through local affiliates remains largely unchanged. That is, despite heightened credit risk, banks' lending via foreign subsidiaries is remarkably persistent. This pattern aligns with anecdotal evidence from Russia following its invasion of Ukraine. More than three years after the initial invasion, Citigroup is still winding down its operations in Russia. Meanwhile, two other large internationally active banks, Raiffeisen Bank International (RBI) and UniCredit, continue to operate their Russian subsidiaries, despite mounting political and regulatory pressure to exit.
Banks' behaviors in response to geopolitical risk appear distinct from their reactions to other forms of country risk. We examine how banks adjust their cross-border and local exposures to increases in broad country risk, using measures commonly employed in the literature, including Hassan et al., 2023's CRI, Ahir et al., 2022's WUI, and sovereign credit default swap (CDS) spreads. The first two measures, constructed using a methodology similar to our CGPR indices, capture broad perceptions of risk or uncertainty. Unlike geopolitical risk, which prompts banks to reduce cross-border lending while maintaining local operations, broad country and sovereign risk do not induce similarly asymmetric adjustments. This divergence underscores banks' unique responses to geopolitical instability.
To explain these empirical findings, we develop a stylized model in which a bank allocates investment between domestic and foreign markets, with foreign exposure taking one of two forms: cross-border lending or local affiliate operations. The key distinction is that affiliates raise funds through local deposits, which are not repaid if geopolitical risk materializes, as historical episodes of conflict often heighten the risk of expropriation. When this risk materializes fully, the foreign government may seize the bank's local affiliate and extinguish its liabilities to local depositors. This asymmetry in liability structure alters banks' incentives, making affiliate-based lending less exposed to net losses from geopolitical shocks.4 As a result, banks adjust cross-border and affiliate exposures differently in response to geopolitical risk--reducing the former while maintaining the latter. By contrast, broader economic risks, despite potentially generating losses, do not impair the enforceability of foreign liabilities and therefore lead to more uniform adjustments across modes of operation.
The model also generates a new prediction about foreign operations: Banks that rely more heavily on foreign funding are less likely to divest from local investments in response to geopolitical risk. We confirm this empirically and further show that, unlike geopolitical risk, local funding positions do not significantly affect how banks adjust foreign exposures to macroeconomic and sovereign risks.
In addition to shaping foreign exposures, the model has implications for domestic lending through spillover channels. It predicts that geopolitical risk abroad can tighten domestic credit supply, particularly for banks with affiliate operations in affected countries. To test this, we analyze the effect of geopolitical risk on banks' domestic corporate loan origination using FR Y-14 data and our BGPR indices. We conduct the analysis at both the loan level, which enables us to control for potential demand-side responses by firms using firm-time fixed effects, and the bank level, to evaluate whether this effect is substantial enough to be observed in aggregate. Both analyses show that U.S. banks originate fewer loans to domestic firms in response to an increase in BGPR.
We further test the role of banks' cross-border versus local exposure in driving these spillover effects. We decompose the BGPR indices into two components: one capturing geopolitical risk from countries where banks operate only cross-border, and another from countries where they maintain local affiliates. We find that the effects on domestic loan origination are significant only for BGPR stemming from countries where banks maintain branches or subsidiaries, confirming the model's prediction and aligning with the earlier finding on the persistence of local claims.
Additionally, we examine how banks' capital positions influence spillover effects. Consistent with the model's prediction, better-capitalized banks reduce domestic lending less in response to foreign geopolitical risk. Moreover, we find that these spillovers are triggered more by perceived threats than realized events, underscoring the role of uncertainty in the transmission channel and reinforcing the model framework.
Because FR Y-14 data cover less than 15 years, we extend our analysis using confidential responses from the Senior Loan Officer Opinion Survey (SLOOS), available since 1990. This survey captures banks' self-reported changes in credit standards--tightening or loosening--as well as shifts in credit demand. We find that increases in BGPR significantly tighten lending standards for domestic C&I loans--especially for banks with affiliate exposure abroad--further confirming the impact of geopolitical risk on U.S. credit supply.
Our findings show that geopolitical risk abroad can reduce domestic credit supply through the global operations of internationally active banks. However, this should not be interpreted as evidence that global banking is inherently harmful. The other side of this dynamic is that international linkages allow domestic shocks to be absorbed through foreign operations, so shocks are naturally transmitted in both directions (Shen and Zhang, 2024). Furthermore, the international banking literature highlights several benefits of cross-border banking. For instance, banks facilitate the efficient allocation of capital across countries (Niepmann, 2015) and export advanced technologies to reduce the cost of financial services (Niepmann, 2023).
Related Literature. A growing body of literature explores the economic and financial effects of geopolitical risk, following the seminal work of Caldara and Iacoviello, 2022 who introduce the geopolitical risk index used in this paper. They show that heightened geopolitical risk reduces aggregate investment and employment. At the firm level, Wang et al.,2019 find that geopolitical risk lowers corporate investment. However, research on banks' responses to geopolitical risk remains limited. The most closely related study, Pham et al., 2021, finds that Ukrainian banks operating in the conflict-affected regions after 2014 reduced lending elsewhere in Ukraine. De Haas et al., 2025 find that banks reduce cross-border lending in response to violent conflicts but increase lending to military-related sectors within the affected countries. Pradhanet al., 2025 also find a reduction in cross-border lending in response to geopolitical tensions between countries, highlighting interaction effects with monetary policy. Other studies show that geopolitical risk constrains bank credit growth (Demir and Danisman,2021), weakens bank stability (Phan et al., 2022), and reduces profitability (Alsagr and Almazor, 2020), primarily by curbing household lending.
Other related work examines the effects of sanctions--a specific policy response to geopolitical events--on bank lending, including Mamonov et al., 2022, Drott et al., 2024, and Danisewicz et al., 2025. Efing et al., 2023's particularly relevant study finds that German banks reduced lending to sanctioned countries from domestic operations but not necessarily from foreign affiliates, especially those in jurisdictions with weak enforcement, suggesting an enforcement-avoidance mechanism under targeted sanctions. By contrast, we find no evidence that U.S. banks increased intragroup lending to affiliates in countries with heightened geopolitical risk. Instead, we highlight a distinct internal mechanism--rooted in funding structure and capital regulation--that explains the persistence of affiliate exposures. Thus, our results highlight a new and complementary channel through which geopolitical risk shapes global banking.
Beyond banking, research on the economic effects of geopolitical power and risk has focused on the impact of geopolitical events--particularly the U.S.-China trade war--on global supply chains (see, e.g., Amiti et al., 2020, Fajgelbaum et al., 2020, Fajgelbaum et al., 2021, Alfaro and Chor, 2023). Clayton et al.,2023 develop a model explaining how geopolitical power and economic coercion shape global financial and real activity.
In addition to the literature on geopolitical risk, our paper contributes to research on the international transmission of shocks through global banks (see, e.g., Peek and Rosengren, 2000, Schnabl, 2012, Cetorelli and Goldberg, 2012a, Ivashina et al., 2015, Hale et al., 2020, Shen and Zhang, 2024). Methodologically, our approach is similar to that of Temesvary and Wei, 2024, who show that U.S. banks with greater exposure to foreign markets affected by COVID-19 reduced domestic C&I lending more sharply. Relatedwork also examines how different forms of global uncertainty influence credit supply. For instance, Correa et al., 2023 analyze how U.S. banks' exposure to trade uncertainty through their borrowers influences bank lending, while Federicoet al., 2025 show that trade shocks can trigger broad contractions in lending by raising non-performing loans.
A relevant theme in this literature is that the mode of foreign operations influences the transmission of shocks. Fillat et al., 2023 find that shock transmission is stronger through branches than subsidiaries due to differences in funding structures. Dell'Ariccia and Marquez, 2010 argue that higher expropriation risk makes subsidiaries less attractive in politically unstable countries. However, we find that the branch-versus-subsidiary distinction does not play a central role in shaping banks' responses to geopolitical risk. Instead, we highlight the broader distinction between cross-border and local affiliate lending--whereby the latter encompasses both branches and subsidiaries--as the key margin along which banks adjust exposures, with important implications for spillovers.5
Our paper also contributes to the literature on risk and capital flows (see, e.g.,Rey, 2016, Kalemli-Özcan, 2019, Jiang et al., 2020, Akinci et al., 2022). Hassan et al., 2023 construct country risk measures from firms' earnings call transcripts and show that heightened risk reduces capital flows. We build on this approach by applying similar textual analysis to develop a new geopolitical risk measure. Related work examines how risk affects cross-border bank lending (e.g., Correa et al., 2022, Bruno and Shin, 2015). Choi and Furceri, 2019 find that rising country-level uncertainty reduces both cross-border lending and borrowing from affected countries.
U.S. banks are exposed to geopolitical risk abroad through their foreign operations. To understand the extent of this exposure, we examine data from the FFIEC 009 report, which provides detailed information on U.S. banks' foreign assets and liabilities by country.6 The FFIEC 009 reporters consist of U.S. banks, bank holding companies (BHCs), and intermediate holding companies (IHCs) holding $30 million or more in claims on residents of foreign countries. We focus on reporters whose ultimate parent bank is in the United States, relying on information from the National Information Center to identify each reporter's ultimate parent bank and its location. Our sample runs from 1986:Q1 to 2022:Q4 and consists of 67 banks in an average period.
Figure 1 illustrates the size, mode, and geographical distribution of U.S. banks' foreign operations. Panel (a) of Figure 1 shows that the share of U.S. banks' foreign assets in total assets averages about 20 percent over the sample period.Larger banks tend to be the most internationally active (Buch et al., 2011; Niepmann, 2023), contributing disproportionately to this aggregate share.
Panel (b) illustrates the mode of U.S. banks' foreign operations. It displays the share of foreign exposures held in foreign offices (either branches or subsidiaries), referred to as "local exposures." The remaining share, known as "cross-border exposures," represents the share of foreign exposures whereby the U.S. parent offices lend directly to foreign residents.7 The figure shows that approximately half of U.S. banks' operations are conducted through offices abroad, while the other half comprises cross-border operations. The share of foreign operations conducted through local operations increased up to the Global Financial Crisis and declined to about 45 percent in the subsequent years.
Figure 1: U.S. Banks' Foreign Operations
Panels (c) through (f) of Figure 1 provide snapshots of the geographical distribution of U.S. banks' foreign operations around the world. Panels (c) and (d) display the kernel density of the share of foreign operations across four regions--Europe, Asia, Latin America, and the rest of the world--in 2010:Q4 and 2019:Q4, respectively, across U.S. banks. Across all regions, there is significant heterogeneity in the extent of exposure among banks. For example, in 2010:Q4, roughly the same number of banks had nearly zero exposure as had 60 percent of their total exposure to Europe. Moreover, this degree of heterogeneity changes over time. By 2019:Q4, fewer banks had more than 60 percent of their exposure in Europe.
Panels (e) and (f) provide more granular snapshots of the geographical distribution of foreign claims for selected banks, displaying their top five countries of exposure in 2010:Q4 and 2019:Q4, using the public version of the FFIEC 009/009a data.8 These snapshots reveal substantial variation acrossbanks in both the geographical composition and the magnitude of their foreign exposure. Moreover, both the origins and magnitudes of exposure shift over time within individual banks, reflecting the fluid nature of foreign banking operations.
Overall, Figure 1 demonstrates that U.S. banks have substantial exposure to a diverse range of countries worldwide, with a significant portion of this exposure stemming from their operations within these countries. These foreign operations expose them to global geopolitical risks. Moreover, since the origin and magnitude of these exposures vary markedly among banks, there is considerable variation in their exposure to geopolitical risk, and this variation also changes over time with each bank. These cross-sectional and time-series variations in foreign exposure are incorporated into the bank-specific measures of geopolitical risk we subsequently construct and play a key role in the identification strategy we apply in the empirical analysis.
Constructing the BGPR index. To measure the extent of U.S. banks' exposure to geopolitical risk through their foreign operations, we construct a bank-specific geopolitical risk index. This BGPR index captures the geopolitical risk each bank faces based on the geography of its foreign lending activities. For each bank $$b$$ and quarter $$t$$, we calculate the index by weighting the geopolitical risk of country $$c$$ (CGPR) by the share of the bank's total assets exposed to that country. We then sum the weighted CGPR indices over all countries.
Specifically, we compute:
| $$\displaystyle BGPR_{bt}=\sum_{c} \omega_{bct-1} CGPR_{ct},$$ | (1) |
where
| $$\displaystyle \omega_{bct-1}=\frac{1}{4} \left(\sum^{4}_{i=1} \frac{exp_{bct-i}}{\sum_{c} asset_{bct-i}}\right),$$ |
and $$exp_{bc}$$ denotes bank $$b$$'s total exposure in country $$c$$, encompassing both cross-border and local claims that the bank has toward the residents of the respective country.
As defined in Equation (1), the BGPR index is more sensitive to changes in geopolitical risk in country $$c$$ when bank $$b$$ has a larger operation in that country.9
CGPR indices. A key component of the BGPR index is CGPR, for which we use two measures. The first is from Caldara and Iacoviello, 2022, who construct a country-specific geopolitical risk index for 44 countries (including the United States). We use the authors' recent CGPR index, which is based on ten newspapers and begins in 1985, rather than the "historical" index, which is based on three newspapers and available from 1900 onward. This set of indices captures perceptions of geopolitical risk from media coverage, reflecting how geopolitical events are reported and emphasized across different news sources over time. We denote Caldara and Iacoviello, 2022's CGPR index as $$CGPR^{N}$$.
We construct a second measure of CGPR to capture firms' perceptions of geopolitical risk, building on [Hassan et al., 2019,Hassan et al., 2023]'s natural language processing method. This approach uses the NL Analytics platform, developed by the authors' team, to apply textual analysis to nearly 400,000 earnings-call transcripts from about 14,000 public companies worldwide, starting in 2002. A crucial step in constructing the CGPR index involves identifying instances in which conference call discussions focus on geopolitical risk in particular countries. To do this, we compile a dictionary of words associated with geopolitical threats and actions, as well as a database of terms identifying the 43 foreign countries of interest, primarily major cities. To count toward our measure of geopolitical risk for a given country, words from both sets must appear in the same sentence. The dictionary of geopolitical risk-related words is extracted from Caldara and Iacoviello, 2022 to allow for a close alignment with $$CGPR^{N}$$. Appendix Table A. lists the search query for geopolitical risk, which is organized into eight categories. Following Caldara and Iacoviello, 2022, each category includes a search query consisting of two sets of words: The first set contains topic words (e.g., "war," "military," "terrorist"), and the second set contains "threat" words for five categories and "act" words for three categories.
Specifically, we construct the CGPR index based on earnings-call transcripts, denoted as $$CGPR^{T}$$, as follows:
| $$\displaystyle CGPR^{T}_{ct} = \frac{1}{F_{ct}}\sum_f \frac{GPRCount_{fct}}{N_{ft}},$$ |
where $$GPRCount_{fct}$$ denotes the number of geopolitical risk-related sentences in the transcript of firm $$f$$ pertaining to country $$c$$ at time $$t$$, $$N_{ft}$$ denotes the total number of sentences in the earnings-call transcript of firm $$f$$ at time $$t$$, and $$F_{ct}$$ denotes the number of firms in country $$c$$ at time $$t$$. The index is designed to be flexible, enabling closer examinations of various dimensions of geopolitical risk for a given country. For instance, we decompose the index into two components: geopolitical risk arising from threats ( $$CGPR^{T(Threat)}_{ct}$$) and from acts ( $$CGPR^{T(Act)}_{ct}$$). We also construct a sub-index focused on the geopolitical risk perceived by financial firms ( $$CGPR^{T^{fin}}_{ct}$$).
We construct BGPR indices using both $$CGPR^{N}$$ and $$CGPR^{T}$$. The index based on $$CGPR^{N}$$ serves as our baseline measure of geopolitical risk due to its longer sample period starting in 1985. The index based on $$CGPR^{T}$$ is used to assess the robustness of our results and to further explore how the components of geopolitical risk drive these results, utilizing the various sub-indices of $$CGPR^{T}$$ that we construct.
Panel (a) of Figure 2 shows the two CGPR indices, aggregated to the global level (GGPR) and normalized by their respective standard deviations within the sample, from 2002:Q1 to 2023:Q4. $$GGPR^{N}$$ (top) and $$GGPR^{T}$$ (bottom) both spike around the onset of three major geopolitical events: the Iraq War in 2003:Q1, the Russia-Ukraine War in 2022:Q1, and the Israel-Hamas War in 2023:Q4. We compare these geopolitical risk indices to two well-known risk indices: Hassan et al., 2023's CRI and Ahir et al., 2022's WUI. The CRI is a measure of broad risk perception constructed using the same data and methodology as our $$CGPR^{T}$$ index; the WUI is a measure of uncertainty constructed by counting the frequency of synonyms for risk or uncertainty using the country reports of the Economist Intelligence Unit. As shown in Panel (b) of Figure 2, both the CRI and WUI spike primarily during periods of significant economic uncertainty, including the height of the Global Financial Crisis around 2008:Q4, the peak of the European sovereign debt crisis in 2011, and the onset of COVID-19 in 2022:Q1. The correlations between the GGPR indices and these two broad risk indices are either low or negative, suggesting that the geopolitical risk captured by $$CGPR^N$$ and $$CGPR^T$$ is a distinct form of risk.
We also compare the CGPR indices to other risk indices at the country level. Appendix Figure A.1 shows these indices for three countries: Poland (Panel (a)), the United Kingdom (Panel (b)), and South Korea (Panel (c)). Charts in the left panel illustrate $$CGPR^{N}$$ (top), $$CGPR^{T}$$ (middle), and $$CGPR^{T(Fin)}$$ (bottom), while the right panel displays three broad risk indices for these countries: $$CRI$$, $$WUI$$, and 5-year sovereign CDS spreads. Similarly to the aggregated global indices, the CGPR indices show sharp increases around significant adverse geopolitical events, including the Russia-Ukraine War that started in 2022 for Poland, a series of terrorist incidents in London in 2005 and 2007 for the United Kingdom, and periods of heightened geopolitical tensions in South Korea due to North Korea's withdrawal from the Nuclear Nonproliferation Treaty in 2003 and missile tests in 2017. Notably, many of these events are specific to the respective country rather than global (e.g., the CGPR indices for South Korea did not spike with the outbreak of the Russia-Ukraine War). By contrast, the broad risk indices for these countries spike primarily during major economic crises, many of which are global. These examples further highlight that our geopolitical risk indices capture a distinct form of risk.
Figure 2: Global Geopolitical Risk and Other Risk Indices
Based on Equation (1), we construct BGPR indices using $$CGPR^{N}$$ and $$CGPR^{T}$$, producing $$BGPR^{N}$$ and $$BGPR^{T}$$, respectively. Appendix Figure A.2 illustrates these two indices at the 25th, 50th, and 75th percentiles over time. The differences among these percentiles reveal significant variation in the level of the index across banks, driven by the heterogeneity in the geography of U.S. banks' foreign operations. Furthermore, these cross-sectional differences evolve substantially over time across
banks.
Given that the goal of our analysis is to understand the effect of geopolitical risk on U.S. banks' foreign and domestic operations, we construct variables that capture the outcomes of interest. To do this, we utilize a variety of regulatory datasets collected by the Federal Reserve.
Bank foreign exposure by country. We use the FFIEC 009 data, which were also used to construct our geopolitical risk indices, to capture the margins of foreign exposure adjustment in response to geopolitical risk. These margins of adjustment include exposure through cross-border and local claims.
Loan-level data. For more granular information on U.S. banks' foreign and domestic operations, we use quarterly loan-level data from the FR Y-14 reports. These reports have been filed confidentially by all BHCs participating in official Federal Reserve bank stress tests since late 2012. The participating institutions report detailed information on individual C&I loans exceeding $1 million, including the borrower's name, country, and industry, as well as the loan amount, origination date, and the probability of default assigned by the bank.10 The probability of default information allows us to study how geopolitical risk affects U.S. banks' assessment of credit risk for exposed loans. Additionally, the loan origination data enables us to analyze the transmission of geopolitical risk to domestic lending.
Bank lending standards. We use data from the Federal Reserve's Senior Loan Officer Opinion Survey to construct additional outcome variables related to U.S. banks' lending standards. In the quarterly survey, the Federal Reserve asks banks about changes in their lending standards and the demand for credit over the previous three months. The aggregate results are published on the Federal Reserve's website, while bank-level responses from 1990 onward are available to researchers in the Federal Reserve System. Banks' responses are recorded on a scale of one to five. Following standard practice in the literature, we transform these responses into three outcome categories: 1 = loosening, 0 = unchanged, and -1 = tightening.
To map SLOOS reporters with corresponding FFIEC 009 reporters, we identify whether a SLOOS-reporting entity is a subsidiary of a BHC that reports the FFIEC 009. If so, we aggregate the responses of all loan officers within that BHC. We focus on lending standards for C&I loans to large and medium-sized enterprises, in line with the predominant loan composition in the FR Y-14 data.
Bank balance sheet information. We supplement our database with quarterly balance sheet data from FR Y-9C and Call Reports, which provide detailed information on the income statements and balance sheets of all U.S. banks. Using these data, we construct a set of bank-level control variables for our regressions, including a bank's Tier 1 capital ratio and liquid-asset ratio.11
Macro, financial, and other data. In addition to bank-level information, we construct country-level macro and financial variables from a variety of data sources for use as control variables. This includes countries' stock price indices and exchange rates from Bloomberg, sovereign CDS spreads from IHS Markit, and sanction status from the Global Sanctions Database.
In this section, we examine how geopolitical risk abroad affects banks' foreign exposures and how they adjust in response. We present three key findings: (i) Geopolitical risk increases the credit risk of U.S. banks with foreign operations; (ii) these banks continue to lend to high-risk countries, despite rising credit risk, through their branches and subsidiaries, while reducing cross-border lending to these countries; and (iii) banks do not adjust their foreign exposures in a similarly asymmetric way in response to other types of risk.
When a country's geopolitical risk increases, the credit risk associated with banks' claims on that country is likely to rise as well. In response, banks are expected to assign a higher probability of default to their exposures to borrowers from that country. We begin our analysis by testing this conjecture, using data from the FR Y-14 reports for the sample period 2013:Q1 through 2022:Q4.
Bank-country level evidence. We first conduct the analysis at the bank-country level. Using the quarterly FR Y-14 data, we compute the average probability of default (PD) of C&I loans to country $$c$$ held by bank $$b$$ at time $$t$$. The PDs are weighted by loan size, using the committed loan amounts. To isolate changes in the probability of default for existing loans--rather than shifts driven by banks originating safer loans--we exclude loans originated in quarter $$t$$.
With the weighted-average PD variable, we study the relationship between CGPR indices and credit risk at the bank-country-time level using the specification:
| $$\displaystyle ln(PD_{bct}) = \beta CGPR_{ct} + \alpha_{bt} + \alpha_{bc} + \epsilon_{bct},$$ | (2) |
where $$PD_{bct}$$ denotes the weighted average probability of default assigned by bank $$b$$ to loans to residents of country $$c$$ at time $$t$$, $$CGPR$$ denotes $$CGPR^{N}$$ or $$CGPR^{T}$$, and $$\alpha_{bt}$$ and $$\alpha_{bc}$$ denote bank-time and bank-country fixed effects, respectively. Standard errors are clustered at the country-time level.
Table 1: Geopolitical Risk and Credit Risk
| $$ln(PD_{bct/bt})$$ | Bank-country Level (1) | Bank-country Level (2) | Bank Level (3) | Bank Level (4) |
|---|---|---|---|---|
| $$CGPR^N_{ct}$$ | 0.100**, (0.040) | |||
| $$CGPR^T_{ct}$$ | 0.076**, (0.032) | |||
| $$BGPR^N_{bt}$$ | 0.134***, (0.024) | |||
| $$BGPR^T_{bt}$$ | 0.215***, (0.042) | |||
| Bank-country FE | Yes | Yes | No | No |
| Bank-time FE | Yes | Yes | No | No |
| Bank FE | No | No | Yes | Yes |
| Time FE | No | No | Yes | Yes |
| Observations | 9588 | 8890 | 411 | 411 |
| $$R^{2}$$ | 0.680 | 0.679 | 0.871 | 0.871 |
Note: This table reports regressions with log average weighted probability of default ($$PD$$) as the dependent variable using data from FR Y-14 for the sample period 2013:Q1through 2022:Q4. Columns (1) and (2) report results from regressions at the bank-country-time level based on Equation (2). $$CGPR^{N}$$ denotes the (recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022. $$CGPR^{T}$$ denotes the country-specific geopolitical risk index constructed based on earnings call transcripts using the NL Analyticsplatform. Columns (3) and (4) report results from regressions at the bank-time level based on Equation (4). $$BGPR^N$$ and $$BGPR^T$$ denote the bank-specific geopolitical risk indices based on $$CGPR^{N}$$ and $$CGPR^{T}$$, respectively. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the country and time level in Columns (1) and (2) and the bank and time level in Columns (3) and (4). *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Columns (1) and (2) of Table 1 present the results. Banks assign higher probabilities of default to existing loans made to borrowers in countries with increasing geopolitical risk, as measured by either $$CGPR^{N}$$ or $$CGPR^{T}$$. A one-standard-deviation increase in $$CGPR$$ raises the weighted average probabilities of default of these loans by 8 to 10 percent. These results support the conjecture that banks perceive higher credit risk in loans to borrowers from countries facing rising geopolitical risk.
Event study. To further investigate how banks adjust their assigned probabilities of default in response to increasing geopolitical risk, we conduct an event study focused on Russia's annexation of Crimea in 2013:Q4 and its invasion of Ukraine in 2022:Q1. These two major geopolitical shocks provide a natural setting to analyze how banks reassess the credit risk of their outstanding exposures to Russia relative to other countries.
Specifically, we run the regression:
| $$\displaystyle ln(PD_{bct}) =\sum_{k\geq-m}\delta_{0k} D_{t}^{k}+\sum_{k\geq-m} \delta_{1k} D_{t}^{k} \times R_{c}+\theta_{bc}+\gamma_{bt}+\epsilon_{bct},$$ | (3) |
where $$PD_{bct}$$ denotes the average probability of default of loans of bank $$b$$ in country $$c$$ at time $$t$$, $$D_{t}^{k}$$ denotes dummy variables that take the value 1 if the geopolitical risk shock occurred $$k$$ quarters following the event and 0 otherwise, $$R_{c}$$ denotes dummy variables that take the value 1 if the borrower country is Russia and 0 otherwise, $$\theta_{bc}$$ denotes bank-country dummies, and $$\gamma_{bt}$$ denotes bank-time dummies.12 The coefficients $$\delta_{1k}$$ capture the differential effect of the two Russia-related geopolitical risk shocks on the average probability of default of loans to Russia compared with loans to other countries in the $$k$$ quarters following the shocks. For this analysis, we restrict the loan sample to all ongoing loans by U.S. banks that have foreign claims on Russia.
Figure 3 plots the coefficients $$\delta_{1k}$$ from Equation (3). It shows that the credit risk of the loans to Russian borrowers increased significantly more than that of loans to borrowers from all other countries in response to the two adverse geopolitical risk shocks. While credit risk did not significantly change across countries on average in the post-shock period, we observe a sharp increase in the average probability of default of outstanding loans to Russian borrowers in the quarter immediately following the shock, and this effect persists for several additional quarters. The magnitude of the increase three quarters after the shock is about two standard deviations of the average probability of default measure, or 20 basis points. This result further confirms that banks attribute greater credit risk to their exposures to borrowers from countries facing escalating geopolitical risk.
Figure 3: Geopolitical Risk and Credit Risk: Russia-Ukraine Conflicts
Aggregate bank-level evidence. Given the bank-country-level and event study evidence, a key question is whether the increases in credit risk following adverse geopolitical risk shocks are substantial enough to materially affect banks' aggregate loan portfolios. To address this, we assess whether an increase in BGPR predicts a rise in the probability of default of a bank's aggregate C&I loan portfolio. Specifically, we compute the weighted-average probability of default for each bank $$b$$'s entire C&I loan portfolio in quarter $$t$$. We then regress the measure (in log) on the BGPR indices, controlling for bank characteristics, bank fixed effects, and time fixed effects:
| $$\displaystyle ln(PD_{bt}) = \beta BGPR_{bt} + \gamma X_{bt} + \alpha_{b} + \alpha_{t} + \epsilon_{bt},$$ | (4) |
where $$BGPR_{bt}$$ denotes $$BGPR_{bt}^N$$ or $$BGPR_{bt}^{T}$$, and $$X_{bt}$$ denotes bank-level control variables including a bank's lagged Tier 1 capital ratio and liquid-asset ratio.
Columns (3) and (4) of Table 1 report the results. An increase in BGPR, as measured by either $$BGPR^N$$ or $$BGPR^T$$, significantly increases the aggregate probability of default of bank loans. A one-standard-deviation increase in BGPR raises the probability of default of a bank's C&I loan portfolio by 13 to 22 percent.
Taken together, the evidence at the bank-country level, from specific events, and at the bank level shows robustly that banks assign a higher probability of default to their exposures to borrowers from countries experiencing increasing geopolitical risk, and that the increase in credit risk is substantial enough to materially affect banks' aggregate loan portfolios.
How do banks respond to the increased riskiness of their loan portfolios as a result of rising geopolitical risk? Do they de-risk? We investigate how banks adjust their foreign exposures in response to increasing geopolitical risk in the countries where they operate, using the FFEIC 009 data for the sample period 1986:Q1 through 2022:Q4.
Specifically, we run the following regression:
| $$\displaystyle \ln(exp_{bct})=\beta_1 CGPR_{ct}+\beta_2 CGPR_{ct-1}+\beta_2 X_{ct}+\beta_3 X_{ct-1}+\alpha_{bt}+\alpha_{bc}+\epsilon_{bct},$$ | (5) |
where $$exp_{bct}$$ represents a measure of bank $$b$$'s exposure to country $$c$$ in quarter $$t$$, and $$CGPR_{ct}$$ stands for $$CGPR^{N}$$ or $$CGPR^{T}$$. We include both the contemporaneous and one-quarter lagged values of $$CGPR$$.13 $$X_{ct}$$ captures country-level macro control variables, including the log of the exchange rate of country $$c$$'s currency vis-à-vis the U.S. dollar, the log of country $$c$$'s main stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States. We also control for bank-time fixed effects ( $$\alpha_{bt}$$) to account for changes in banks' foreign exposures common to all countries, and bank-country fixed effects ( $$\alpha_{bc}$$) to account for level differences in exposures of banks across countries. Standard errors are clustered by country and time.
Table 2 reports the results with $$CGPR^{N}$$ as the main regressor. Columns (1) and (2) present results from regressions with banks' log total foreign exposures as the dependent variable. Columns (3) and (4) and Columns (5) and (6) are based on log cross-border and local exposures as the dependent variables, respectively. As described in Section 2, banks can extend credit to foreign borrowers through two modes of operation: from an office outside the borrower's country of residence, resulting in cross-border claims, or from an office located in the borrower's country, resulting in local claims. The odd-numbered columns show the baseline results, and the even-numbered columns add country-level macro controls.
The results show that while banks reduce their total exposure to countries experiencing increasing geopolitical risk, their reallocation behavior varies significantly depending on their mode of operation in the affected country. While banks reduce cross-border exposures to countries facing escalating geopolitical risk, their operations through local offices in those countries remain largely unchanged.14 A one-standard-deviation increase in $$CGPR^{N}$$ reduces cross-border exposure by 6 percent (Column 4). By contrast, the corresponding coefficients for local claims are small and not statistically significant (Column 6).15 The resultsare quantitatively and qualitatively similar with $$CGPR^{T}$$ as the main regressor, as shown in Appendix Table B.1.
Table 2: Response of Banks' Foreign Operations to Geopolitical Risk
| $$ln(exp_{bct})$$ | Total (1) | Total (2) | Cross-border (3) | Cross-border (4) | Local (5) | Local (6) |
|---|---|---|---|---|---|---|
| $$CGPR^N_{ct}$$ | -0.018**, (0.007) | -0.022***, (0.008) | -0.026***, (0.008) | -0.031***, (0.008) | 0.011, (0.015) | 0.010, (0.015) |
| $$CGPR^N_{ct-1}$$ | -0.010, (0.008) | -0.010, (0.008) | -0.014, (0.009) | -0.013, (0.009) | 0.012, (0.014) | 0.009, (0.014) |
| $$\boldsymbol{1}$$ $$(Sanction)_{t}$$ | 0.007, (0.017) | -0.020, (0.018) | -0.009, (0.027) | |||
| $$ln(Exch. Rate)_{t}$$ | -0.002, (0.025) | 0.004, (0.025) | -0.187*, (0.109) | |||
| $$ln(Stock Index)_{t}$$ | -0.125***, (0.046) | -0.117**, (0.046) | -0.113, (0.088) | |||
| $$ln(Exch. Rate)_{t-1}$$ | -0.064**, (0.032) | -0.068**, (0.032) | 0.129, (0.106) | |||
| $$ln(Stock Index)_{t-1}$$ | 0.152***, (0.049) | 0.146***, (0.049) | 0.213**, (0.086) | |||
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 137312 | 108303 | 135803 | 106891 | 34801 | 31039 |
| $$R^{2}$$ | 0.894 | 0.906 | 0.875 | 0.887 | 0.878 | 0.885 |
Note: This table reports results from regressions at the bank-country-time level based on Equation (5) using the FFEIC 009 data for the sample period 1986:Q1 through 2022:Q4. $$CGPR^N$$ denotes the(recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022. The dependent variable is the log total foreign claims in Columns (1) and (2), log cross-border claims in Columns (3) and (4), and log local claims in Columns (5) and (6). Columns (1), (3), and (5) show the baseline results for each dependent variable. Columns (2), (4), and (6) add country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States. All regressions include bank-country and bank-time fixed effects. $$CGPR^N$$ is standardized by its respective standard deviation within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
UniCredit, RBI, and Citigroup have deliberately reduced their cross-border operations with Russia while continuing to operate their Russian subsidiaries, consistent with the empirical evidence presented earlier. UniCredit's and RBI's 2022:Q2 earnings presentations explicitly describe this strategy: Both banks emphasize efforts to reduce cross-border exposures through early repayment and proactive client management, while maintaining locally funded subsidiary operations. As UniCredit's CEO stated, "Our Russia exposure has been reduced further at minimum cost. [...] Net cross-border exposures were reduced...mainly as a result of proactive discussions with clients producing early repayment at nominal value. The [Russian] subsidiary is robust and performing well." Despite mounting regulatory and political pressure--including a 2024 ECB directive requiring banks to present plans for exiting or reducing their Russian operations--both UniCredit and RBI have continued maintaining their subsidiaries.17 These developments underscore that even under acute geopolitical stress, global banks tend to preserve affiliate-based lending while retreating from cross-border exposures.18
This pattern--the retrenchment of cross-border lending alongside the persistence of local operations in response to geopolitical risk--also emerges in aggregate data. We track the evolution of cross-border and local claims on Russia following three major geopolitical events: the conflict with Georgia in 2008:Q3, the annexation of Crimea in 2013:Q4, and the invasion of Ukraine in 2022:Q1. Panel (a) of Appendix Figure B.3 presents the claims by the U.S. banking sector on Russia, and Panel (b) presents those for all BIS-reporting banking sectors. Notably, while both local and cross-border claims on Russia declined after these geopolitical shocks, local exposures fell significantly less, in percentage terms, than cross-border exposures.
In sum, our regression results indicate that while banks primarily reduce cross-border exposures to countries facing heightened geopolitical risk, they largely maintain existing loans within their local operations despite the rising credit risk. This persistence aligns with anecdotal evidence on banks' responses to Russia's 2022 invasion of Ukraine and patterns observed in the raw data.
Do banks adjust their foreign operations similarly to other forms of country risk? Or is geopolitical risk distinct? We explore these questions by examining how banks adjust their cross-border and local exposures in response to other types of risks. We run Equation (5) using broad country-specific risk indices (instead of $$CGPR$$) as the main regressor, replacing $$CGPR$$ with Hassan et al., 2023's CRI, Ahir et al., 2022's WUI, and sovereign CDS spreads.
Table 3 reports the regression results. Columns (1)-(2), (3)-(4), and (5)-(6) correspond to specifications using CRI, WUI, and CDS spreads as the key regressors, respectively. Odd-numbered columns use log cross-border claims as the dependent variable, while even-numbered columns use log local claims. The results for CRI suggest a positive relationship with cross-border and local claims, though the effect of country risk on cross-border claims is not statistically significant. For WUI, the coefficients on both cross-border and local claims are small and statistically insignificant, suggesting that foreign exposures exhibit little sensitivity to broad country-level uncertainty. The results for CDS spreads show a negative relationship with cross-border and local claims, though only the effect on local claims is marginally significant, while the effect on cross-border claims remains insignificant.19
Overall, the results suggest that country risk, uncertainty, and sovereign credit risk do not have strong or consistent effects on banks' cross-border and local exposures, in contrast to the clear and asymmetric response observed with geopolitical risk. While banks reduce cross-border exposures but maintain local exposures in response to geopolitical risk, their adjustments to other types of risk do not follow this pattern. Instead, the effects of country risk, uncertainty, and sovereign credit risk on cross-border and local claims appear weaker and less systematic, with no clear distinction in how banks adjust these two types of exposures.
Discussion. Our findings indicate that banks respond differently to geopolitical risk than to other financial and economic risks, reinforcing the idea that geopolitical risk is a distinct category of risk.
One possible reason for this distinction is that geopolitical risk often entails expropriation risk. Throughout history, geopolitical conflicts have led to the seizure of foreign bank assets, making expropriation a uniquely catastrophic feature of geopolitical risk. Notable examples include the 1917 Russian Bolshevik Revolution, during which the new government nationalized the financial system, expropriating all foreign-owned banks. During World War II, Germany expropriated foreign-owned banks, including Austria's Kreditanstalt; and Japan took control of Allied-associated banks operating in occupied territories across Southeast Asia. In 1957, following the 1956 Suez Crisis, Egypt nationalized British and French banks in retaliation for military intervention. In 1960, after the Cuban Revolution, the government nationalized all U.S. banks, seizing the assets of Citibank, Chase Manhattan, and First National City Bank. From 2008 to 2010, Venezuela, under Hugo Chávez, nationalized Banco de Venezuela, previously owned by Spain's Santander, as part of broader policies to expand state economic and industrial control. From 2023 to 2025, Russia seized assets from U.S. and European banks, including JPMorgan and Deutsche Bank, through a series of court rulings and state actions. While framed partly as legal responses to Western sanctions, these moves also reflected broader geopolitical retaliation, underscoring the rising legal and political risks foreign banks face in conflict-affected jurisdictions.
Beyond outright seizure, geopolitical risk may also involve softer forms of state intervention that nonetheless pose major challenges for foreign banks. These include capital controls, profit repatriation limits, sudden regulatory shifts, asset freezes, and windfall taxes targeting foreign institutions. Though less extreme than nationalization, such actions can trap capital, reduce operational flexibility, and generate legal and compliance uncertainty--risks that are difficult to anticipate or hedge.
Because all forms of expropriation risk are typically accompanied by erosion of the rule of law and erratic policy shifts, geopolitical risk becomes far harder for banks to manage using standard risk-assessment tools. This sets it apart from conventional financial or economic risks, which are generally more predictable and manageable within established institutional frameworks.
While expropriation risk may be a distinctively catastrophic feature of geopolitical risk, it remains an open question whether it helps explain the asymmetric response of banks' cross-border and local exposures. In the next section, we formalize expropriation risk within a global banking framework and show that it can account for the divergence in how banks adjust their foreign exposures under geopolitical risk.
Table 3: Other Country Risks and Banks' Foreign Operations
| $$ln(exp_{bct})$$ | (1) Cross-border | (2) Local | (3) Cross-border | (4) Local | (5) Cross-border | (6) Local |
|---|---|---|---|---|---|---|
| $$CRI_{ct}$$ | -0.004, (0.017) | 0.021, (0.017) | ||||
| $$CRI_{ct-1}$$ | 0.008, (0.016) | 0.036**, (0.018) | ||||
| $$WUI_{ct} $$ | 0.004, (0.005) | 0.003, (0.007) | ||||
| $$WUI_{ct-1} $$ | -0.007, (0.005) | 0.004, (0.007) | ||||
| $$CDS_{ct}$$ | -0.013, (0.009) | -0.028*, (0.016) | ||||
| $$CDS_{ct-1}$$ | -0.004, (0.012) | -0.022, (0.014) | ||||
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 53655 | 18940 | 127821 | 33810 | 60464 | 19961 |
| $$R^{2}$$ | 0.917 | 0.904 | 0.876 | 0.877 | 0.914 | 0.902 |
Note: This table reports results from regressions at the bank-country-time level based on Equation (5) with alternative country-specific risk indices as the main regressor (instead of $$CGPR$$).The alternative indices include CRI by Hassan et al., 2023 (Columns (1) and (2)), WUI by Ahir et al., 2022 (Columns (3) and (4)), and sovereign CDS spreads (Columns (5) and (6)). The dependent variable is the log cross-border claims in Columns (1), (3), and (5), and log local claims in Columns (2), (4), and (6). All regressions include bank-country and country-time fixed effects. All the risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
In this section, we present a stylized model to rationalize the empirical facts established in the previous section and generate testable qualitative predictions on the transmission of geopolitical risk to domestic credit through global banks for the subsequent analysis. The model examines banks' choices to operate abroad via cross-border lending or local affiliates, as well as their domestic operations, and analyzes how credit allocation across these channels responds to heightened foreign geopolitical risk. We focus on expropriation risk as a distinctive feature of geopolitical risk: As detailed in Section 3.3, both historical and recent geopolitical conflicts are often accompanied by the seizure of foreign bank assets or by other forms of state intervention, making expropriation risk a salient element in shaping global banks' responses to geopolitical shocks.
The framework consists of three periods and a global bank that makes investment decisions. At $$t=0$$, the bank decides how much to invest abroad and at home. It can invest a fixed amount $$L^*$$ abroad for two periods and a variable amount $$L$$ domestically for one period, with the option to reinvest in domestic assets at $$t=1$$.
The return on the foreign two-period investment is uncertain. At $$t=0$$, the probability of success is high ($$p^G$$) with probability $$(1-\phi)$$ and low ($$p^B$$) with probability $$\phi$$. These good (G) and bad (B) states correspond to states of low and high geopolitical risk, respectively. At $$t=1$$, the bank learns whether geopolitical risk is high or low, which determines the probability of success of its foreign investment: If geopolitical risk is high, the probability of success is low and $$p = p^B$$; if geopolitical risk is low, the probability of success is high and $$p = p^G$$. At $$t=2$$, geopolitical risk either materializes or does not. If geopolitical risk does not materialize, the foreign investment succeeds and pays $$R^*$$. If geopolitical risk materializes, it leads to expropriation by the foreign government: The government seizes the investment, resulting in a zero payoff. For simplicity, we do not model domestic geopolitical risk. Domestic investment is assumed to be risk-free, yielding a guaranteed return of $$R$$ at both $$t=1$$ and $$t=2$$.
The bank has an initial equity endowment $$E_1$$ at $$t=0$$ and is subject to a leverage constraint that closely follows the formulation of minimum regulatory capital ratios under Basel III. Specifically, the bank's equity-to-risk-weighted assets ratio must remain above a constant threshold $$\mu$$:
| $$\displaystyle \frac{E_1}{L_1+L^* \alpha(\phi, p^G, p^B)}\geq \mu,$$ | (6) |
where $$\alpha(\phi, p^G, p^B)>1$$ is the risk weight on the foreign investment $$L^*$$, which decreases with $$\phi$$, $$p^G$$, and $$p^B$$.
The effect of heightened geopolitical risk abroad on capital constraints in the model maps actual regulatory practice. As shown in Section 3.1, geopolitical risk increases the probability of default on loans extended to borrowers in affected countries. Since default probability directly influences the risk weight assigned toloans, rising geopolitical risk results in higher capital requirements for foreign exposures.20 By contrast, the risk weight on the domestic, risk-free investment is set to 1.
We assume $$L^*<\frac{E_1}{ \alpha \mu}$$ ensuring that the fixed foreign investment $$L^*$$ does not exceed the bank's total lending capacity given the risk weights on foreign assets and allowing room for domestic investment. Additionally, we assume that foreign investment is preferable to investing solely in the domestic asset, which holds if $$(1-\phi) p^G R^*$$ is sufficiently high.
Because $$L^*$$ and $$E_1$$ are fixed, the equity constraint pins down $$L_1$$:
| $$\displaystyle L_1=\frac{E_1-\mu L^* \alpha(\phi, p^G, p^B)}{\mu}.$$ |
To finance its investments, the bank borrows $$D_1=L_1+L^*-E_1$$ from depositors at an exogenous interest rate $$i<R$$.21 The funding is for one period but can be rolled over at $$t=1$$ at the same rate. At $$t=1$$, the bank learns the probability of success of its foreign investment and may choose to liquidate early, recovering $$\delta L^*$$, where $$\delta <1$$. This option allows the bank to withdraw from foreign operations in response to rising geopolitical risk, albeit at a cost. While early liquidation results in a direct loss, it eliminates risk exposure and reduces risk-weighted assets, thereby enhancing the bank's lending capacity.
Two modes of foreign operations. The bank can choose between two modes of foreign operation: cross-border investment ($$X$$), whereby it lends directly from its home country, or local investment ($$A$$), whereby it lends through a locally established affiliate in the foreign country. Note that establishing a local affiliate incurs a non-pecuniary fixed cost $$\kappa>0$$.22 When conducting cross-border operations, the bank raises funding domestically. By contrast, when operating from a local affiliate, it raises funding $$D_t^*$$ in the foreign market, where $$D_t^*<D_t$$, while borrowing the remainder $$D_t-D_t^*$$ at home.23 We assume that the foreign and domestic interest rates on deposits are the same.
The key distinction between the two modes--aside from the fixed cost $$\kappa$$--is that foreign deposits, unlike domestic deposits, are not repaid if the geopolitical risk materializes at $$t=2$$. The rationale is that when geopolitical conflict leads to expropriation, the foreign government seizes the bank's local affiliate, and the bank is no longer obligated to repay foreign depositors. As a result, the expected profits from operating a local affiliate at $$t=1$$ exceed those from cross-border investment by $$(1-p) D^*_2 i$$.
This assumption that local liabilities are repaid under expropriation is consistent with how market participants and financial institutions assess risk exposure in the context of geopolitical crises. Analysts and bank disclosures have highlighted the importance of funding segmentation between parent banks and their foreign affiliates. For example, at the onset of the Russia-Ukraine war, UniCredit was a net borrower from its Russian subsidiary, which limited the bank's losses in a worst-case expropriation scenario because it would not be expected to repay the intragroup loan.24 Similarly, RBI's 2022:Q1 financial report emphasizes the strategic importance of local self-funding in managing geopolitical risk: "Naturally, we did not foresee a military conflict such as the one we are currently witnessing. We have however...ensured that [RBI's subsidiaries] are self-financing, allowing only a restricted amount of cross-border financing."
The assumption of full expropriation is intended to capture a uniquely salient feature of geopolitical risk and to illustrate its most stark implications; however, the core mechanism remains valid under softer forms of expropriation risk. For example, instead of assuming zero-recovery expropriation when geopolitical risk materializes at $$t=2$$, it is sufficient to assume that repatriating profits becomes costly. In such cases, raising local funding still reduces the parent's exposure to these frictions by allowing the affiliate to meet its obligations locally, preserving the incentive to operate through affiliates rather than via cross-border lending. Thus, the mechanism whereby local funding mitigates parent-level downside risk does not rely on the strict assumption of zero-recovery expropriation.
Additional mechanisms may be relevant, particularly in settings where outright expropriation is viewed as unlikely. For example, one could assume that interest rates abroad are lower, incentivizing banks to establish affiliates to access cheaper local funding at a fixed cost, while liquidation costs are higher for investments made through affiliates than for cross-border lending. While many of the model's predictions continue to hold under this alternative, some do not.25 Nonetheless, these additional features may help capture banks' incentives in countries facing elevated geopolitical tensions but maintaining strong legal institutions. We focus on expropriation risk given its historical and empirical prominence in major geopolitical episodes, while acknowledging that other mechanisms may also influence banks' decisions across a broader range of geopolitical environments.
Having established the key differences between the two modes of foreign operation, we now solve the model to analyze how the bank adjusts its cross-border and affiliate investments in response to heightened geopolitical risk, explaining the empirical findings presented in the previous section.
Under liquidation, profits realized at $$t=2$$ are the same across both modes. This follows because $$\delta$$ is identical in both cases, and investments in the domestic asset at $$t=0$$ and $$t=1$$ are the same. Specifically, $$\pi_2^{X, L}=\pi_2^{A, L}=R L^L_2-i D^L_2$$, where $$L^L_2$$ denotes the investment in the domestic asset at $$t=1$$ under liquidation ($$L$$). Investment decisions remain unchanged because they are governed by the leverage constraint, which is independent of $$D_t$$ (and $$D^*_t$$).
When the foreign investment continues ($$C$$), the bank's expected profits under cross-border investment are:
| $$\displaystyle \pi_2^{X, C}=p R^* L^*+L^C_2 R-D^C_2 i.$$ | (7) |
The bank's expected profits when it continues operating through a local affiliate are:
| $$\displaystyle \pi_2^{A, C}=p R^* L^*+L^C_2 R-D^C_2 i+(1-p) D^*_2 i>\pi_2^{X, C}.$$ | (8) |
Note the superscript associated with $$p$$ is suppressed because the formulas hold for both the good and bad states of the world.
Equations (7) and (8) highlight a key implication of the model: Because local deposits raised by foreign affiliates do not have to be repaid if the foreign government expropriates the affiliate, the bank has a stronger incentive to liquidate cross-border investment than investment through a foreign affiliate amid heightened geopolitical risk.
PROPOSITION 1. Let $$\hat{\delta}$$ denote the threshold value of $$\delta$$ at which the bank is indifferent between liquidating or continuing its foreign investment at t = 1.
(a) Since $$\pi^{A,C}_2 > \pi^{X,C}_2$$ and $$\pi^{X,L}_2 = \pi^{A,L}_2$$ , it follows that $$\hat{\delta}^{A}$$ > $$\hat{\delta}^{X}$$. In other words, the threshold $$\delta$$ required for liquidation is higher when the bank operates through a foreign affiliate than when it invests cross-border.
(b) The difference in liquidation thresholds, $$\Delta\hat{\delta}$$ = $$\hat{\delta}^{A}$$- $$\hat{\delta}^{X}$$, increases as p decreases. That is, the lower the probability of success p, the larger the difference between the two liquidation thresholds.
(c) The difference in liquidation thresholds, $$\Delta\hat{\delta}$$ = $$\hat{\delta}^{A}$$- $$\hat{\delta}^{X}$$, increases as $$D^*_2$$ increases. That is, the more funding the bank raises in the foreign market, the larger the difference between the two liquidation thresholds.
Proof. See Appendix C.
Proposition 1 shows that, for the same liquidation cost $$\delta$$, banks are less likely to liquidate investments in a foreign affiliate than in cross-border operations. The model thus explains the empirical finding from Section 3.2 that banks reduce exposures primarily through cross-border lending, while maintaining affiliate-based lending when geopolitical risk rises.26
Furthermore, Proposition 1(b) stipulates that as geopolitical risk increases (reflected in a lower $$p$$), the divergence between liquidation decisions for cross-border and affiliate investments becomes more pronounced, which helps explain the empirical finding from Section 3.3 that geopolitical risk is distinct from other types of risk. Expropriation risk plays a uniquely catastrophic role in shaping how banks adjust their cross-border and local operations. When sovereign or economic risk rises, banks may incur losses, but operations typically continue and obligations to foreign creditors remain. As a result, banks' responses to sovereign and economic risk tend to be more symmetric across cross-border and local operations, unlike the asymmetric adjustment observed under geopolitical risk.
Empirical validation. We further validate the model by empirically testing Proposition 1(c), which predicts that the more funding a bank raises in the foreign market, the less it divests from local investments in that market in response to geopolitical risk. To test this, we gather data on local liabilities from FFIEC 009 and augment Equation (5) with interaction terms between $$CGPR$$ and banks' laggedlocal liability position, measured as four-quarter moving averages (in log). The coefficient on this interaction term estimates the extent to which a larger local funding position influences the sensitivity of foreign exposure to geopolitical risk.
Panel (a) of Table 4 presents the results. Columns (1) and (2) present results from regressions with banks' log total foreign exposures as the dependent variable, and Columns (3) and (4) and Columns (5) and (6) are based on log local and cross-border exposures as the dependent variables, respectively, with the even-numbered columns including macro control variables. With total foreign exposures as the dependent variable, our coefficients of interest on the interaction terms are positive and significant, indicating that banks with larger local funding positions are less likely to reduce their overall foreign exposures in response to heightened geopolitical risk. This effect is primarily driven by local exposures, as shown in Columns (3) and (4), where the coefficients on the interaction terms remain positive and statistically significant. By contrast, the coefficients in Columns (5) and (6), where cross-border exposures are the dependent variable, are not statistically different from zero, suggesting that the mitigating effect of local funding applies specifically to local investments rather than cross-border positions. All these findings support the model's prediction.
Panel (b) of Table 4 further examines whether local funding positions influence banks' foreign lending responses to other types of risk, as measured by CRI, WUI, and sovereign CDS spreads. The results show that, unlike in the case of geopolitical risk, local funding positions do not significantly affect how banks adjust their foreign exposures when faced with these alternative risks. This finding reinforces the model's prediction that geopolitical risk, particularly due to expropriation concerns, uniquely alters banks' foreign lending behavior. It also highlights that the ability to default on foreign liabilities plays a central role in banks' responses to geopolitical risk but is less relevant when responding to other macroeconomic or financial risks.27
Table 4: Banks' Foreign Response to Risk by Ex Ante Local Liabilities (a) Geopolitical Risk
| $$ln(exp_{bct})$$ | Total Exp. (1) | Total Exp. (2) | Local (3) | Local (4) | Cross-border (5) | Cross-border (6) |
|---|---|---|---|---|---|---|
| $$CGPR^N_{ct}$$ | -0.049***, (0.019) | -0.050***, (0.017) | -0.067***, (0.022) | -0.066***, (0.022) | -0.074***, (0.015) | -0.071***, (0.013) |
| $$CGPR^N_{ct}$$ $$\times$$ $$ln(LL)_{bct-1}$$ | 0.004**, (0.002) | 0.004**, (0.002) | 0.008**, (0.004) | 0.008**, (0.004) | 0.002, (0.002) | 0.002, (0.002) |
| $$CGPR^N_{ct-1}$$ | -0.018, (0.016) | -0.019, (0.015) | -0.034, (0.026) | -0.034, (0.026) | -0.027*, (0.015) | -0.023, (0.015) |
| $$CGPR^N_{ct-1}$$ $$\times$$ $$ln(LL)_{bct-2}$$ | 0.002, (0.002) | 0.002, (0.002) | 0.005, (0.005) | 0.005, (0.005) | -0.001, (0.002) | -0.001, (0.002) |
| Macro Controls | No | Yes | No | Yes | No | Yes |
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 16829 | 16107 | 15870 | 15208 | 16040 | 15374 |
| $$R^{2}$$ | 0.956 | 0.958 | 0.919 | 0.922 | 0.938 | 0.938 |
Table 4: Banks' Foreign Response to Risk by Ex Ante Local Liabilities (b) Other Risks
| $$ln(exp_{bct})$$ | CRI (1) Local | CRI (2) Cross-border | WUI (3) Local | WUI (4) Cross-border | CDS (5) Local | CDS (6) Cross-border |
|---|---|---|---|---|---|---|
| $$ln(exp_{bct})$$ | Local | Cross-border | Local | Cross-border | Local | Cross-border |
| $$CRI_t$$ | -0.025, (0.033) | -0.019, (0.035) | ||||
| $$CRI_t$$ $$\times$$ $$ln(LL)_{bct-1}$$ | 0.002, (0.004) | -0.003, (0.004) | ||||
| $$CRI_{t-1}$$ | -0.010, (0.032) | -0.059*, (0.033) | ||||
| $$CRI_{t-1}$$ $$\times$$ $$ln(LL)_{bct-2}$$ | 0.004, (0.004) | 0.005, (0.004) | ||||
| $$WUI_t$$ | -0.004, (0.015) | 0.030**, (0.012) | ||||
| $$WUI_t$$ $$\times$$ $$ln(LL)_{bct-1}$$ | -0.000, (0.002) | -0.006***, (0.002) | ||||
| $$WUI_{t-1}$$ | 0.021, (0.015) | 0.002, (0.013) | ||||
| $$WUI_{t-1}$$ $$\times$$ $$ln(LL)_{bct-2}$$ | -0.002, (0.002) | -0.003, (0.002) | ||||
| $$ln(CDS)_t$$ | 0.004, (0.086) | -0.067, (0.096) | ||||
| $$ln(CDS)_t$$ $$\times$$ $$ln(LL)_{bct-1}$$ | -0.004, (0.012) | 0.007, (0.007) | ||||
| $$ln(CDS)_{t-1}$$ | -0.167*, (0.087) | 0.083, (0.086) | ||||
| $$ln(CDS)_{t-1}$$ $$\times$$ $$ln(LL)_{bct-2}$$ | 0.008, (0.012) | 0.008, (0.007) | ||||
| Macro Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 12631 | 12521 | 14490 | 14347 | 13982 | 13803 |
| $$R^{2}$$ | 0.943 | 0.922 | 0.940 | 0.922 | 0.941 | 0.922 |
Note: This table reports results from regressions at the bank-country-time level based on an augmented version of Equation (5), using the FFEIC 009 data for the sample period 1986:Q1 through 2022:Q4. Panel (a) uses $$CGPR^N$$, the (recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022, along with $$ln(LL)_{bct-1}$$, the log of local liabilities received by bank $$b$$ from country $$c$$, calculated as a four-quarter moving average from $$t-4$$ to $$t-1$$, and their interactions as the main regressors. The dependent variable is the log total foreign claims in Columns (1) and (2), log local claims in Columns (3) and (4), and log cross-border claims in Columns (5) and (6). Columns (1), (3), and (5) show the baseline results for each dependent variable. Columns (2), (4), and (6) add country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States. Panel (b) replaces $$CGPR^N$$ with alternative country-specific risk indices, $$ln(LL)_{bct-1}$$, and their interactions as the main regressors. The alternative indices include Hassan et al., 2023's CRI (Columns (1) and (2)), Ahiret al., 2022's WUI (Columns (3) and (4)), and log sovereign CDS spreads (Columns (5) and (6)). The dependent variable is log local claims in Columns (1), (3), and (5), and log cross-border claims in Columns (2), (4), and (6). All regressions include bank-country and country-time fixed effects. All risk indices are standardized by their respective standard deviation within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Next, we use the model to analyze the implications of rising geopolitical risk abroad for domestic lending. The bank's equity position and the riskiness of its investments determine its domestic lending at $$t=1$$. When the bank liquidates its foreign investment, its equity is given by $$E^L_2=\delta L^*+R_1L_1- D_1 i$$, where $$R_1L_1- D_1 i$$ captures earnings from domestic investment at $$t=1$$. If the bank does not liquidate, its equity is $$E^C_2=L^*+R_1L_1- D_1 i$$, which satisfies $$E^C_2>E^L_2$$, indicating that liquidation results in a lower equity position. Although liquidation reduces the bank's equity, it also frees up leverage capacity, as the risk weight on domestic investment is 1, whereas the risk weight on the riskier foreign investment is higher. As a result, domestic lending following liquidation is given by:
| $$\displaystyle L^L_2=\frac{\delta L^*+R_1 L_1- D_1 i}{\mu}.$$ |
If geopolitical risk turns out to be high at $$t=1$$ and the bank does not liquidate, the bank's borrowing capacity shrinks relative to the good state of the world due to an increase in foreign risk-weighted assets $$L^* \alpha(p)$$:
| $$\displaystyle L^{C}_2=\frac{L^*+R_1L_1- D_1 i-\mu L^* \alpha(p)}{\mu}.$$ |
The effects of geopolitical risk on domestic lending are summarized in the following proposition:
PROPOSITION 2.
(a) $$L^{G,C}_2 > L^{B,C}_2$$ . Domestic lending under continuation is higher in the good state of the world with low geopolitical risk than in the bad state with high geopolitical risk.
(b) $$L^{L}_2 > L^{B,C}_2$$ if $$\delta > 1 - \alpha(p)\mu$$. Domestic lending is higher when the bank liquidates its foreign investment at $$t = 1$$ than when it continues its foreign operation, provided that the reduction in borrowing capacity from higher foreign risk-weighted assets due to geopolitical risk exceeds the combined effect of the equity loss and the decrease in risk-weighted assets under liquidation.
(c) $$L_1 > L^{B,C}_2$$ if $$\frac{(R_{1}-1)L_{1}-(i-1)D_{1}}{\mu}$$ < $$(\alpha(p^B) - \alpha(\phi, p^B, p^G))L^*. L^{G,C}_2 > L_1 $$ always holds. In other words, domestic lending contracts at $$t = 1$$ in the bad state of the world relative to $$t = 0$$ if the positive effect of increased equity from domestic investment realized in $$t = 1$$ on leverage is sufficiently small relative to the increase in foreign risk-weighted assets. Domestic lending always expands in the good state of the world.
Proof. See Appendix C.
Proposition 2 highlights that heightened geopolitical risk abroad reduces domestic lending when banks do not divest, creating spillover effects from foreign geopolitical risk into domestic credit supply. Whether domestic lending is higher under liquidation or continuation of the foreign investment depends on the cost of liquidation. When banks liquidate foreign investment, they free up lending capacity due to lower risk weights and, at the same time, reallocate lending capacity from the foreign to the home country. As long as the liquidation cost is relatively low and banks can recover sufficient capital, the negative spillover effects on domestic credit supply will be limited. As a result, these spillover effects tend to be smaller under liquidation than under continuation. Since banks with foreign affiliates are less likely to liquidate, spillover effects tend to be stronger for banks operating through affiliates than through cross-border lending.
Furthermore, when geopolitical risk increases, domestic lending will decline relative to the previous period--unless banks generate sufficient domestic profits to counteract the negative spillover effects. Lower capital requirements can also help mitigate these spillovers. Banks typically hold capital buffers above the regulatory minimum, providing some flexibility to absorb shocks without immediately constraining lending. Instead of depending on regulatory intervention to ease capital requirements, banks may choose to draw down their excess buffers to sustain domestic lending in the face of heightened geopolitical risk.
Model predictions. From our theoretical framework, we derive the following testable hypotheses on the spillover of geopolitical risk into domestic lending through global banks:
Guided by the model predictions from the previous section, we test the spillover effects of geopolitical risk on domestic lending through global banks. Our main part of this analysis examines how U.S. banks' exposure to foreign geopolitical risk, as measured by the BGPR indices, affects their loan origination to U.S. firms, using FR Y-14 data.
Loan-level analysis. To test the prediction that banks exposed to heightened geopolitical risk in their foreign operations reduce domestic lending more (Prediction 1 from Section 4), we first estimate the following specification at the loan level using the FR Y-14 data for the period 2013:Q1 through 2022:Q4:
| $$\displaystyle ln(orig_{bit})=\beta BGPR_{bt} + \delta Z_{bt}+\delta X_{bit}+\gamma_{it} + \alpha_b+\epsilon_{bit},$$ | (9) |
where $$orig_{bit}$$ denotes the amount of loan origination by bank $$b$$ to domestic firm $$i$$ at time $$t$$, $$BGPR_{bt}$$ denotes $$BGPR_{bt}^N$$ or $$BGPR_{bt}^{T}$$, $$Z_{bt}$$ denotes bank-level controls including liquid-asset ratio and Tier 1 capital ratio, $$X_{bit}$$ denotes loan-level controls including maturity and interest rate, $$\gamma_{it}$$ denotes firm-time fixed effects, and $$\alpha_b$$ denotes bank fixed effects.28 The regression sample is restricted to loans by U.S.-headquartered banks to U.S. firms.
Our coefficient of interest, $$\beta$$, measures the extent to which banks that experienced a greater increase in geopolitical risk through their foreign exposures, as captured by the BGPR indices, adjusted their loan origination to domestic firms, conditioning on the specified controls and fixed effects. As described in Section 2, the BGPR indices contain considerable variation, both across banks and over time, due to differences in the geographical origin and magnitude of their exposures, both of which fluctuate over time. Our estimation relies exclusively on cross-bankwithin-firm variation for identification, given the inclusion of firm-time fixed effects. This alleviates concerns about confounding factors from the demand side, such as changes in credit demand by firms in response to geopolitical risk.
Panel (a) of Table 5 reports the results. Columns (1) through (4) presents estimates using $$BGPR^N$$ as the main regressor, while Columns (5) through (8) use $$BGPR^T$$. Columns (1) and (5) include bank and firm-time fixed effects and incorporate both bank- and loan-level controls. The remaining columns further include alternative risk controls including bank-specific risk indices based on CRI (Columns (2) and (6)), WUI (Columns (3) and (7)), and sovereign CDS spread (Columns (4) and (8)), which are constructed following Equation (1).
The results show that U.S. banks significantly reduce loan origination to domestic firms in response to an increase in BGPR, whether measured by $$BGPR^N$$ or $$BGPR^T$$. The inclusion of firm-time fixed effects indicates that changes in credit demand are not a significant confounding factor. The coefficients remain stable when alternative risk controls are included, indicating that the effect of geopolitical risk on loan origination is not confounded by broader measures of financial and economic risk. This finding is consistent with our illustrations and results from Sections 2 and3.3, which highlight that geopolitical risk is distinct from other types of risk. The consistency of these estimates across the two measures and various model specifications further reinforces the robustness of the results, confirming that the impact of geopolitical risk on lending is not driven by firm-level credit demand shocks but rather by banks' adjustments in credit supply. Based on the estimates in Columns (1) and (5), a one-standard-deviation increase in $$BGPR$$ reduces U.S. banks' loan origination to U.S. firms by 8 to 9 percent.
Table 5: Geopolitical Risk and U.S. Domestic Loan Origination (a) Loan Level
| $$ln(orig_{bit})$$ | $$BGPR^N$$ (1) | $$BGPR^N$$ (2) | $$BGPR^N$$ (3) | $$BGPR^N$$ (4) | $$BGPR^T$$ (5) | $$BGPR^T$$ (6) | $$BGPR^T$$ (7) | $$BGPR^T$$ (8) |
|---|---|---|---|---|---|---|---|---|
| $$BGPR^N_{bt}$$ | -0.087***, (0.027) | -0.061**, (0.029) | -0.089***, (0.027) | -0.087***, (0.027) | ||||
| $$BGPR^T_{bt}$$ | -0.081***, (0.020) | -0.061***, (0.022) | -0.083***, (0.020) | -0.081***, (0.020) | ||||
| $$BCRI_{bt}$$ | 0.072**, (0.032) | 0.069**, (0.032) | ||||||
| $$BWUI_{bt}$$ | -0.044, (0.030) | -0.047, (0.030) | ||||||
| $$BCDS_{bt}$$ | 0.001, (0.024) | 0.005, (0.024) | ||||||
| Bank Controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Loan Controls | No | Yes | Yes | No | Yes | Yes | Yes | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm-time FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| $$N$$ | 175943 | 175943 | 175943 | 175943 | 175943 | 175943 | 175943 | 175943 |
| $$R^{2}$$ | 0.617 | 0.617 | 0.617 | 0.617 | 0.617 | 0.617 | 0.617 | 0.617 |
Table 5: Geopolitical Risk and U.S. Domestic Loan Origination (b) Bank Level
| $$ln(orig_{bt})$$ | $$BGPR^N$$ (1) | $$BGPR^N$$ (2) | $$BGPR^N$$ (3) | $$BGPR^N$$ (4) | $$BGPR^T$$ (5) | $$BGPR^T$$ (6) | $$BGPR^T$$ (7) | $$BGPR^T$$ (8) |
|---|---|---|---|---|---|---|---|---|
| $$BGPR^N_{bt}$$ | -0.073, (0.062) | -0.095, (0.071) | -0.072, (0.062) | -0.078, (0.063) | ||||
| $$BGPR^N_{bt-1}$$ | -0.177**, (0.074) | -0.185**, (0.073) | -0.160**, (0.066) | -0.185**, (0.072) | ||||
| $$BGPR^T_{bt}$$ | -0.045, (0.069) | -0.066, (0.073) | -0.042, (0.068) | -0.053, (0.070) | ||||
| $$BGPR^T_{bt-1}$$ | -0.175**, (0.070) | -0.172**, (0.068) | -0.163**, (0.068) | -0.163**, (0.073) | ||||
| Bank Controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| AltRisk Controls | No | CRI | WUI | CDS | No | CRI | WUI | CDS |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| $$N$$ | 475 | 475 | 475 | 475 | 475 | 475 | 475 | 475 |
| $$R^{2}$$ | 0.955 | 0.955 | 0.956 | 0.955 | 0.956 | 0.957 | 0.957 | 0.956 |
Note: This table reports results with log loan origination amount ($$orig$$) as the dependent variable, using FR Y-14 data from 2013:Q1 through 2022:Q4. Panel (a)reports results from loan-level regressions based on Equation (9). Panel (b) reports results from bank-level regressions based on Equation (10). $$BGPR^N$$ denotes the bank-specific geopolitical risk index, constructed from $$CGPR^{N}$$ or the (recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022 accordingto Equation (1). $$BGPR^T$$ denotes the bank-specific geopolitical risk index derived from $$CGPR^{T}$$, which is based on earnings call transcripts processed through the NL Analytics platform, capturing geopolitical risk perception by firms worldwide. Bank controls include Tier 1 capital ratio, liquid-asset ratio as well as their lagged versions in bank-level regressions. Loan controls include interest rate and maturity. Alternative risk controls include bank-specific risk indices based on the country risk index ($$BCRI$$) by Hassan et al., 2023 and the World Uncertainty Index($$BWUI$$) by Ahir et al., 2022, and sovereign CDS spread ($$BCDS$$), as well as their lagged versions in bank-level regressions. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level for loan-level regressions and at the bank level for bank-level regressions. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Bank-level analysis. In addition to the loan-level analysis, which allows us to control for potential demand-side responses by firms and isolate the supply effect, we conduct a bank-level analysis to assess whether this effect is substantial enough to be observed at the aggregate level. We apply the following specification:
| $$\displaystyle ln(orig_{bt})=\beta_1 BGPR_{bt} + \beta_2 BGPR_{bt-1} + \delta Z_{bt-1} + \gamma_{t} + \alpha_b +\epsilon_{bit},$$ | (10) |
where $$orig_{bt}$$ denotes the total amount of loan origination by bank $$b$$ at time $$t$$, $$BGPR_{bt}$$ denotes $$BGPR_{bt}^N$$ or $$BGPR_{bt}^{T}$$, and the lagged BGPR indices are included to capture any persistent effects. $$Z_{bt}$$ denotes bank-level controls including contemporaneous and lagged liquid-asset ratio and Tier 1 capital ratio, $$\gamma_{t}$$ denotes time fixed effects, and $$\alpha_b$$ denotes bank fixed effects. The coefficients of interest, $$\beta_1$$ and $$\beta_2$$, capture the total spillover effects of foreign geopolitical risk on U.S. banks' domestic loan origination on average.
Panel (b) of Table 5 reports the results. As in Panel (a), Columns (1) through (4) present estimates using $$BGPR^N$$ as the main regressor, while Columns (5) through (8) use $$BGPR^T$$. Columns (1) and (4) include bank and time fixed effects as well as bank-level controls, while the remaining columns further add alternative risk controls, including bank-specific risk indices based on the CRI, WUI, and sovereign CDS spreads.
The coefficients on both $$BGPR^N$$ and $$BGPR^T$$ are negative, significant, and of similar magnitude, indicating a strong relationship between foreign geopolitical risk and domestic credit supply at the bank-level. Based on the estimates in Columns (1) and (5), a one-standard-deviation increase in $$BGPR$$ reduces U.S. banks' loan origination to U.S. firms by 22 to 25 percent on average. This indicates that the spillover effects of foreign geopolitical risk on domestic credit markets through global banks are substantial enough to be observed at the aggregate level.
Taken together, the loan- and bank-level results confirm Prediction 1 from the theoretical framework: Banks exposed to heightened geopolitical risk in their foreign operations reduce domestic lending more. This finding underscores the spillover effects of foreign geopolitical shocks, demonstrating that banks do not simply adjust their foreign operations in response to geopolitical risk but also contract their domestic credit supply.
Next, we test Prediction 2 from the model, which states that the reduction in domestic lending is more pronounced when geopolitical risk rises in markets where banks operate through affiliates. To analyze this, we estimate Equations (9) and (10) using BGPR indices decomposed into two separate components to distinguish between exposure from local claims and cross-border claims:
| $$\displaystyle BGPR_{bt}(\boldsymbol{1}($$Cross-border$$\displaystyle ))$$ | $$\displaystyle = \sum_{c} \boldsymbol{1}($$Cross-border$$\displaystyle )_{bct-1} \times \omega_{bct-1} CGPR_{ct},$$ | (11a) |
| $$\displaystyle BGPR_{bt}(\boldsymbol{1}($$Local$$\displaystyle ))$$ | $$\displaystyle = \sum_{c} \boldsymbol{1}($$Local$$\displaystyle )_{bct-1} \times \omega_{bct-1} CGPR_{ct},$$ | (11b) |
where $$\boldsymbol{1}($$Cross-border$$)_{bct}$$ denotes a dummy variable equal to 1 if bank $$b$$ has no local claims on country $$c$$ at time $$t$$ and 0 otherwise, and $$\boldsymbol{1}($$Local$$)_{bct}$$ is a dummy variable equal to 1 if bank $$b$$ has non-zero local claims on country $$c$$ at time $$t$$ and 0 otherwise. All other variables are consistently defined with Equation (1).
The theory presented in Section 4 suggests that, as long as the hit to equity from liquidation is limited, spillovers from geopolitical risk into domestic lending should be smaller under cross-border lending than under affiliate lending. This is because liquidating cross-border claims frees domestic lending capacity, allowing banks to reallocate credit back to the home country. By contrast, continued local lending raises risk-weighted assets as geopolitical risk increases, tightening capital constraints. Therefore, we expect the coefficients on $$BGPR^N_{bt}\boldsymbol{1}($$Local$$)$$ to be negative and significant, whereas those on $$BGPR^N_{bt}\boldsymbol{1}($$Cross-border$$)$$ should be smaller, if significant at all.
Table 6 presents the results with $$BGPR^N$$ as the main regressor, with Panel (a) displaying the loan-level results and Panel (b) displaying the bank-level results. Columns (1) and (2) include $$BGPR^N_{bt}(\boldsymbol{1}($$Local$$))$$ as the regressor, without and with bank-level controls, respectively; Columns (3) and (4) include $$BGPR^N_{bt}(\boldsymbol{1}($$Cross-border$$))$$ as the regressor; and Columns (5) and (6) include both as regressors. As shown in the first two columns, the coefficients on $$BGPR^N_{bt}(\boldsymbol{1}($$Local$$))$$ are negative and significant, indicating that geopolitical risk, through banks' local exposure, plays a significant role in reducing domestic loan origination and driving the spillover effects. By contrast, the coefficients on $$BGPR^N_{bt}(\boldsymbol{1}($$Cross-border$$))$$ are not statistically significant, suggesting that geopolitical risk transmits to domestic credit supply primarily through local affiliate exposure rather than cross-border operations. When both indices are included in the regression, the coefficient on $$BGPR^N_{bt}(\boldsymbol{1}($$Local$$))$$ continues to be negative and significant, confirming the role of foreign exposure through local claims in driving the spillover effects. These results hold at both the loan and bank levels. Appendix Table B.5 presents the results with $$BGPR^T$$ as the main regressor, and all the results are quantitatively and qualitatively similar.
Overall, these results provide strong evidence that global banks with local affiliate exposure react more significantly to geopolitical shocks abroad, leading to a greater contraction in domestic lending. This finding aligns with the model's Prediction 2, confirming that spillover effects are stronger when geopolitical risk increases in markets where banks have local affiliates. By contrast, banks with predominantly cross-border operations adjust their foreign exposures more quickly and to a greater extent, allowing them to absorb geopolitical shocks with less impact on their domestic lending activity. The distinction between affiliate-based and cross-border exposure highlights the role of global banks' corporate structures in shaping their responses to geopolitical risk and influencing its transmission to the domestic economy.
Table 6: Geopolitical Risk Transmission: Cross-border vs. Local Exposure, $$BGPR^N$$ (a) Loan Level
| $$ln(orig_{bit})$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$BGPR^N_{bt}(\boldsymbol{1}$$(Local)$$)$$ | -0.060**, (0.026) | -0.062**, (0.026) | -0.060**, (0.027) | -0.060**, (0.027) | ||
| $$BGPR^N_{bt}(\boldsymbol{1}$$(Cross-border)$$)$$ | -0.021, (0.044) | -0.037, (0.046) | -0.010, (0.045) | -0.023, (0.046) | ||
| Bank Controls | No | Yes | No | Yes | No | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 205642 | 199753 | 205642 | 199753 | 205642 | 199753 |
| $$R^{2}$$ | 0.594 | 0.592 | 0.594 | 0.592 | 0.594 | 0.592 |
Table 6: Geopolitical Risk Transmission: Cross-border vs. Local Exposure, $$BGPR^N$$ (b) Bank Level
| $$ln(orig_{bt})$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$BGPR^N_{bt}(\boldsymbol{1}$$(Local)$$)$$ | -0.061, (0.061) | -0.075, (0.060) | -0.069, (0.061) | -0.082, (0.060) | ||
| $$BGPR^N_{bt-1}(\boldsymbol{1}$$(Local)$$)$$ | -0.168**, (0.076) | -0.165**, (0.075) | -0.169**, (0.075) | -0.167**, (0.074) | ||
| $$BGPR^N_{bt}(\boldsymbol{1}$$(Cross-border)$$)$$ | -0.175, (0.229) | -0.159, (0.237) | -0.179, (0.234) | -0.160, (0.242) | ||
| $$BGPR^N_{bt-1}(\boldsymbol{1}$$(Cross-border)$$)$$ | -0.108, (0.265) | -0.148, (0.276) | -0.198, (0.288) | -0.238, (0.298) | ||
| Bank Controls | No | Yes | No | Yes | No | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 475 | 461 | 475 | 461 | 475 | 461 |
| $$R^{2}$$ | 0.954 | 0.955 | 0.952 | 0.953 | 0.954 | 0.955 |
Note: This table reports results from regressions with log loan origination amount ($$orig$$) as the dependent variable using data from FR Y-14 for thesample period 2013:Q1 through 2022:Q4. Panel (a) reports results from regressions at the loan level based on Equation (9), using $$BGPR^N_{bt}(\boldsymbol{1}$$(Local)$$)$$ and $$BGPR^N_{bt-1}(\boldsymbol{1}$$(Cross-border)$$)$$, which are constructed based on Equation (5.2). Panel (b)reports results from regressions at the bank level based on Equation (10). Bank-level controls include contemporaneous Tier 1 capital ratio and liquid-asset ratio as well as their lagged versions in bank-level regressions. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
In the following section, we conduct additional analyses to complement the main findings on the spillover effects of geopolitical risk on domestic lending through global banks and to assess robustness. First, we test Prediction 3 from the model, which examines the role of capital constraints. Second, we investigate whether the threat or the realization of geopolitical risk is the primary driver of spillover effects. Third, we analyze how banks' exposure to geopolitical risk influences their lending standards for domestic loans, leveraging SLOOS data, which covers a broader set of banks and extend to the 1990s.
Role of capital constraints and bank profitability. Prediction 3 from the model stipulates that spillover effects are larger for banks with lower capital ratios and profits. To test this, we estimate bank-level regressions with domestic loan origination as the dependent variable and the BGPR indices, along with the interaction of the BGPR indices with either a bank's lagged Tier 1 capital ratio or a bank's lagged return on average assets (ROAA), as the key regressors. If capital constraints or profitability influence the spillover effect of geopolitical risk on domestic loan origination, the coefficients on the interactions should be positive, indicating that banks with stronger capital or profitability positions reduce loan origination less in response to increasing geopolitical risk abroad. The results are reported in Panel (a) of Table ? with Columns (1) and (2) presenting those for capital ratios using $$BGPR^N$$ and $$BGPR^T$$ as the regressor, respectively, and Columns (3) and (4) presenting the results for bank profitability. The coefficients on the interaction terms are positive, supporting the role of capital constraints and bank profitability in mitigating the spillover effects of geopolitical risk.
Table 7: Role of Capital Constraints, Profits, and Type of Geopolitical Risk (a) Capital Constraints and Bank Profitability
| $$ln(orig_{bt})$$ | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| $$BGPR^N_{bt}$$ | -0.824**, (0.342) | -0.100, (0.096) | ||
| $$BGPR^T_{bt}$$ | -0.284, (0.237) | -0.274***, (0.079) | ||
| $$BGPR^N_{bt}$$ x Capital$$_{bt-1}$$ | 0.050**, (0.021) | |||
| $$BGPR^T_{bt}$$ x Capital$$_{bt-1}$$ | 0.011, (0.015) | |||
| $$BGPR^N_{bt}$$ x ROAA$$_{bt-1}$$ | 0.010, (0.036) | |||
| $$BGPR^T_{bt}$$ x ROAA$$_{bt-1}$$ | 0.155***, (0.040) | |||
| Bank Control | Yes | Yes | Yes | Yes |
| Bank FE | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes |
| Observations | 477 | 477 | 477 | 477 |
| $$R^{2}$$ | 0.952 | 0.952 | 0.952 | 0.953 |
Table 7: Role of Capital Constraints, Profits, and Type of Geopolitical Risk (b) Geopolitical Risk Threat versus Act (Loan Level)
| $$ln(orig_{bit})$$ | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| $$BGPR^{T(Threat)}_{bt}$$ | -0.075***, (0.021) | |||
| $$BGPR^{T(Act)}_{bt}$$ | -0.048*, (0.025) | |||
| $$BGPR^{T^{fin}(Threat)}_{bt}$$ | -0.061***, (0.021) | |||
| $$BGPR^{T^{fin}(Act)}_{bt}$$ | -0.026, (0.019) | |||
| Bank Controls | Yes | Yes | Yes | Yes |
| Loan Controls | Yes | Yes | Yes | Yes |
| Bank FE | Yes | Yes | Yes | Yes |
| Firm-time FE | Yes | Yes | Yes | Yes |
| Observations | 171380 | 171380 | 171380 | 171380 |
| $$R^{2}$$ | 0.615 | 0.615 | 0.615 | 0.615 |
Note: This table reports regression results with log loan origination amount ($$orig$$) as the dependent variable using data from FR Y-14 for the sample period 2013:Q1 through 2022:Q4. Columns (1) and (2) of Panel (a) include $$BGPR^N$$ or $$BGPR^T$$, lagged Tier 1 capital ratio, and their respective interactions as key regressors in bank-level regressions. Columns (3) and (4) includes $$BGPR^N$$ or $$BGPR^T$$, lagged return on average assets (ROAA), and their respective interactions as key regressors. $$BGPR^N$$ denotes the bank-specific geopolitical risk index, constructed from $$CGPR^{N}$$ or the (recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022 according to Equation (1). $$BGPR^T$$ denotes the bank-specific geopolitical risk index derived from $$CGPR^{T}$$, which is based on earnings call transcripts processed through the NL Analytics platform, capturing geopolitical risk perception by firms worldwide. Bank control includes lagged liquid-asset ratio for Columns (1) and (2) and, in addition, lagged Tier 1 capital ratio in Columns (3) and (4). Panel (b) reports results from loan-level regressions with subindices of $$BGPR^T$$ as the main regressors. $$BGPR^{T(Threat)}$$ captures firms' perceptions of geopolitical risk threats, and $$BGPR^{T(Act)}$$ captures their perceptions of geopolitical risk stemming from acts. $$BGPR^{T^{fin}(Threat)}$$ and $$BGPR^{T^{fin}(Act)}$$ capture financial firms' perceptions of geopolitical risk stemming from threats and acts, respectively. Bank controls include Tier 1 capital ratio and liquid-asset ratio. Loan controls include interest rate and maturity. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Geopolitical risk: threat versus act. Next, we examine the different dimensions of geopolitical risk to assess whether spillover effects are driven more by the threat or the realization of geopolitical risk. This analysis differentiates the impact of anticipated versus actual geopolitical disruptions and evaluates the validity of the model setup in Section 4, in which geopolitical risk is primarily modeled as arising from the threat rather than its realization.
As described in Section 2, $$BGPR^T$$ is designed to be flexible, enabling decomposition into different components. We construct five subindices of $$BGPR^T$$. $$BGPR^{T(Threat)}$$ is constructed using the component of CGPR that captures firms' perceptions of the threats of geopolitical risk, while $$BGPR^{T(Act)}$$ isolates their perceptions of geopolitical risk arising from realized events (e.g., attacks and wars). Additionally, $$BGPR^{T^{fin}}$$ reflects perceptions of geopolitical risk specifically by financial firms, with $$BGPR^{T^{fin}(Threat)}$$ and $$BGPR^{T^{fin}(Act)}$$ representing the corresponding subcomponents for threats and acts, respectively.
We estimate the impact of each subindex of geopolitical risk on U.S. banks' loan origination to domestic firms using Equation (9) for loan-level regressions and Equation (10) for bank-level regressions. Panel (b) of Table 7 presents the results from the loan-level regressions. Columns (1) through (5) correspond to regressions using $$BGPR^{T(Threat)}$$, $$BGPR^{T(Act)}$$, $$BGPR^{T^{fin}}$$, $$BGPR^{T^{fin}(Threat)}$$, and $$BGPR^{T^{fin}(Act)}$$ as the main regressors, respectively. The results indicate that the effect of BGPR on domestic loan origination is primarily driven by perceived threats of geopolitical risk (Columns (1) and (3)) rather than the realization of specific events (Columns (2) and (4)). This underscores the role of uncertainty in generating the spillover effects of geopolitical risk through banks.
Appendix Table B.6 presents the results from the bank-level regressions, which closely mirror those from the loan-level analysis, further supporting these findings.
Overall, the results show that the threat of geopolitical risk has a stronger influence on lending decisions than realized shocks. Banks preemptively adjust exposures to mitigate potential losses, validating the model framework outlined in Section 4.
Domestic lending standards. To supplement our main analysis on loan origination, we examine the spillover effects of geopoliticalrisk on U.S. banks' domestic lending standards, which have predictive power for loan origination (Niepmann and Schmidt-Eisenlohr, 2023).29 We use survey data from the SLOOS, which, compared with the FR Y-14 used in the loan origination analysis, has the advantage of covering a larger set of banks and extending further back in time, starting in 1990.30
To measure lending standards, we analyze each bank's response to the survey question on whether the bank tightened or loosened credit standards for C&I loans to large and medium-sized enterprises, where higher values indicate greater loosening. As is standard in the literature, we code responses as 1 for loosening, 0 for no change, and -1 for tightening. We regress this variable on the contemporaneous and lagged quarterly change in BGPR, controlling for bank fixed effects as well as macro and bank-level conditions. Following common practice in the literature (e.g., Bassett et al., 2014), we include the first lag of the dependent variable to account for the persistence in SLOOS responses.
The baseline regression equation is specified as follows:
| $$\displaystyle ls_{bt}$$ | $$\displaystyle =$$ | $$\displaystyle \beta_0 ls_{bt-1}+ \beta_1 \Delta \log(BGPR_{bt})+ \beta_2 \Delta \log(BGPR_{bt-1})+\gamma_1 \Delta X_{t}+\gamma_2 \Delta X_{t-1}$$ | (12) |
| $$\displaystyle +\delta_1 Z_{bt}+\delta_2 Z_{bt-1} +\alpha_b+\epsilon_{bt},$$ |
where $$ls_{bt}$$ represents bank $$b$$'s response to the SLOOS survey question on lending standards in quarter $$t$$, and $$BGPR_{bt}$$ denotes the BGPR indices. The macroeconomic controls, $$X_t$$, include the two-year Treasury yield, the slope of the yield curve (10y-2y), the CBOE Volatility Index (VIX), the S&P 500 index, and U.S. industrial production. The BGPR index, VIX, S&P 500 index, and industrial production enter as quarterly log changes, while other variables, except the lagged dependent variable, enter as simple changes. The regression also includes bank fixed effects ($$\alpha_b$$) and controls for changes in loan demand, based on banks' response to the SLOOS survey question on loan demand, as well as their lagged Tier 1 capital ratio and liquid-asset ratio ($$Z_{bt}$$).31
Panel (a) of Table 8 presents the baseline results for the period 1990:Q2 through 2022:Q2.32 Columns (1) through (3) use $$BGPR^N$$ as the main regressor, while Columns (4) through (6) use $$BGPR^T$$. Columns (1) and (4) include bank fixed effects. Columns (2) and (5) add macroeconomic controls, and Columns (3) and (6) further incorporate bank-level controls, including banks' responses to changes in credit demand, as well as their Tier 1 capital and liquid-asset ratios.
Across Columns (1) through (3), the coefficients on $$BGPR^N$$ are negative and statistically significant, often at the 1 percent level, indicating that increased exposure to geopolitical risk, as measured by $$BGPR^N$$, leads to a significant tightening of lending standards for domestic loans. Regarding magnitude, a one-standard-deviation increase in BGPR leads to 2 percent of banks shifting from maintaining unchanged lending standards to tightening them within the same quarter, with an additional 4 percent tightening in the following quarter (Column 3). The results for $$BGPR^T$$ in Columns (4) through (6) are consistent with these findings, reinforcing the conclusion that geopolitical risk affects banks' lending standards. Overall, these results align with the loan origination results in Section 5.1, providing further support for Prediction 1 from the model.
Table 8: Geopolitical Risk and Domestic Lending Standards (a) Baseline
| $$ls_{bt}$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$\Delta \log(BGPR^N_{bt})$$ | -0.023***, (0.008) | -0.015**, (0.007) | -0.023**, (0.011) | |||
| $$\Delta \log(BGPR^N_{bt-1})$$ | -0.019**, (0.008) | -0.014*, (0.008) | -0.037***, (0.012) | |||
| $$\Delta \log(BGPR^T_{bt})$$ | -0.008, (0.011) | -0.032***, (0.011) | -0.034***, (0.012) | |||
| $$\Delta \log(BGPR^T_{bt-1})$$ | -0.005, (0.010) | -0.014, (0.010) | -0.011, (0.010) | |||
| Macro Controls | No | Yes | Yes | No | Yes | Yes |
| Bank Controls | No | No | Yes | No | No | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 3099 | 3050 | 2095 | 1486 | 1486 | 1476 |
| $$R^{2}$$ | 0.235 | 0.294 | 0.331 | 0.258 | 0.339 | 0.352 |
Table 8: Geopolitical Risk and Domestic Lending Standards (b) Role of Local versus Cross-border Foreign Exposures
| $$ls_{bt}$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$\Delta \log(BGPR^N_{bt}$$ ( $$\boldsymbol{1}$$(Local))) | -0.027**, (0.011) | -0.021*, (0.011) | ||||
| $$\Delta \log(BGPR^N_{bt-1}$$ ( $$\boldsymbol{1}$$(Local))) | -0.031***, (0.012) | -0.025**, (0.012) | ||||
| $$\Delta \log(BGPR^N_{bt}$$ ( $$\boldsymbol{1}$$(Cross-border))) | -0.020**, (0.008) | -0.011, (0.009) | ||||
| $$\Delta \log(BGPR^N_{bt-1}$$ ( $$\boldsymbol{1}$$(Cross-border))) | -0.025**, (0.010) | -0.013, (0.011) | ||||
| $$\Delta \log(BGPR^T_{bt}$$ ( $$\boldsymbol{1}$$(Local))) | -0.038***, (0.013) | -0.039***, (0.015) | ||||
| $$\Delta \log(BGPR^T_{bt-1}$$ ( $$\boldsymbol{1}$$(Local))) | -0.010, (0.013) | -0.010, (0.015) | ||||
| $$\Delta \log(BGPR^T_{bt}$$ ( $$\boldsymbol{1}$$(Cross-border))) | -0.004, (0.011) | 0.011, (0.013) | ||||
| $$\Delta \log(BGPR^T_{bt-1}$$ ( $$\boldsymbol{1}$$(Cross-border))) | -0.017*, (0.010) | -0.014, (0.012) | ||||
| Macro Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank Controls | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 1303 | 2067 | 1275 | 1019 | 1264 | 808 |
| $$R^{2}$$ | 0.340 | 0.330 | 0.339 | 0.341 | 0.338 | 0.323 |
Note: This table reports bank-level regression results, where the dependent variable is banks' response to the SLOOS survey question on tightening, maintaining, or loosening credit standards for C&I loans to large andmedium-sized firms, using a sample spanning from 1990:Q2 through 2022:Q2. Panel (a) reports results based on Equation (13), where $$BGPR^N$$ (Columns (1) through (3)) is the bank-specific geopolitical risk index constructed from $$CGPR^{N}$$, the country-specific geopolitical risk index from Caldara and Iacoviello, 2022, using Equation (1). $$BGPR^T$$ (Columns (4) through (6)) is the bank-specific geopolitical risk index derived from $$CGPR^{T}$$, which captures firms'geopolitical risk perceptions based on earnings-call transcripts processed through the NL Analytics platform. Columns (2) and (5) add macroeconomic controls, including (log) changes in the two-year Treasury yield, the yield curve slope (10y-2y), the CBOE Volatility Index (VIX), the S&P 500 index, and U.S. industrial production. Columns (3) and (6) further control for loan demand, as well as banks' liquid-asset and Tier 1 capital ratios. In Panel (b), $$BGPR^N_{bt}$$ ( $$\boldsymbol{1}$$(Local)) and $$BGPR^N_{bt}$$ ( $$\boldsymbol{1}$$(Cross-border)) are constructed following Equation (5.2). All specifications include bank fixed effects, macroeconomic controls, bank-level controls, and the lagged dependent variable as a regressor. For both panels, the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Parallel to the analysis in Section 5.2, which tests Prediction 2 from the model, we investigate whether the effect of BGPR on bank lending standards is driven by exposure through local claims versus cross-border claims. Panel (b) of Table 8 presents the results, confirming that the tightening effect of BGPR on domestic lending standards is primarily driven by banks' foreign local exposures. This finding aligns with our proposed mechanism, confirms Prediction 2 from the model, and mirrors the corresponding results on loan origination.
Following the earlier analysis, we investigate how different dimensions of geopolitical risk influence banks' domestic lending conditions. Appendix Table B.7 reports results using $$BGPR^{T(Threat)}$$, $$BGPR^{T(Act)}$$, $$BGPR^{T^{fin}(Threat)}$$, and $$BGPR^{T^{fin}(Act)}$$ to capture banks' exposure to geopolitical risk. These findings are consistent with our earlier results based on the FR Y-14 data, further confirming that banks respond more strongly to geopolitical risk stemming from perceived threats rather than realized acts.
While we focus primarily on C&I loans, Appendix Table B.8 shows that banks also tighten lending standards on commercial real estate loans in response to geopolitical risk. This finding provides additional evidence that banks contract their domestic credit supply when foreign geopolitical risk increases. Notably, the U.S. commercial real estate sector is less directly affected by geopolitical risk compared with industries such as trade and manufacturing, which are more exposed to risks abroad. Therefore, our findings on spillover effects are unlikely to be driven by credit demand responses.
This paper studies the impact of geopolitical risk on banks' foreign operations and the resulting spillover effects on domestic credit supply. Using a combination of established and newly constructed geopolitical risk indices and multiple supervisory data covering U.S. bank lending activities spanning nearly four decades, we find that geopolitical risk significantly increases these banks' credit risk. Despite this heightened risk, banks continue lending through their foreign branches and subsidiaries while scaling back cross-border lending. This asymmetric response is unique to geopolitical risk, as banks do not adjust their foreign operations in the same way in response to other types of risk.
We develop a stylized model to explain these findings, emphasizing how banks' funding structures and expropriation risk drive their responses to geopolitical risk. The model highlights that foreign affiliates rely on local funding, which helps contain losses in the event of expropriation under geopolitical shocks. By contrast, cross-border lending is funded domestically and remains more directly exposed to geopolitical risk. This distinction in net exposure explains why banks reduce cross-border exposure while maintaining affiliate-based lending.
These forces generate significant spillover effects on domestic credit supply. We show that U.S. banks facing geopolitical risk abroad reduce lending to domestic firms, with the effect strongest when the risk originates in countries where banks operate through local affiliates. This underscores the importance of banks' operational structures in shaping how geopolitical shocks are transmitted to the domestic economy.
Our findings reveal the potential real and distributional consequences of geopolitical risk transmitted through global banks. Constrained firms may respond to reduced credit supply by cutting investment and employment (see, e.g., Chodorow-Reich, 2014, Alfaro et al., 2021). At the same time, credit reallocation can generate amplification effects: Firms with better credit access may shift to smaller domestic lenders, crowding out more marginal borrowers such as small- and medium-sized enterprises. In this way, geopolitical shocks may propagate through the domestic credit system not only via direct exposure but also through general equilibrium effects in financial intermediation--an important area for future research.
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This section provides additional details on the geopolitical risk indices constructed in the paper.
Appendix Table A.1 lists the search query used to construct the earnings-call transcript-based country-specific geopolitical risk index, $$CGPR^T$$. This measure applies the natural language processing method from [Hassan et al., 2019, Hassan et al., 2023] using the NL Analytics platform. Thedictionary of geopolitical risk-related terms is drawn from Caldara and Iacoviello, 2022, ensuring close alignment with the newspaper-based index, $$CGPR^N$$.
Appendix Figure A.1 presents the geopolitical and alternative risk indices for three countries: Poland (Panel (a)), the United Kingdom (Panel (b)), and South Korea (Panel (c)). The left panel of each chart shows $$CGPR^{N}$$ (top), $$CGPR^{T}$$ (middle), and $$CGPR^{T(Fin)}$$ (bottom), the latter focusing on geopolitical risk as perceived by financial firms. The right panel displays three broader risk measures for each country: the Country Risk Index (CRI) from Hassan et al., 2023, the World Uncertainty Index (WUI) from Ahir et al., 2022, and five-year sovereign CDS spreads. The geopolitical risk indices spike around major geopolitical events, while the broader risk indices respond primarily to large economic shocks.
Appendix Figure A.2 plots the 25th, 50th, and 75th percentiles of $$BGPR^{N}$$ and $$BGPR^{T}$$ over time. The dispersion across percentiles highlights substantial cross-bank variation in exposure to geopolitical risk, reflecting differences in the geographic composition of U.S. banks' foreign operations. Moreover, these cross-sectional differences change meaningfully over time, indicating that banks' geopolitical risk exposures are both heterogeneous and dynamic.
Table A.1: Search Query for CGPR Index Based on Earnings-Call Transcripts - Panel A. Search Categories and Search Queries
| Category | Search queries |
|---|---|
| Threats: 1. War threats | War words AND threat words |
| Threats: 2. Peace threats | Peace words AND peace disruption words |
| Threats: 3. Military buildup | Military words AND buildup words |
| Threats: 4. Nuclear threats | Nuclear bigrams AND threat words |
| Threats: 5. Terrorist threats | Terrorist words AND threat words |
| Acts: 6. Beginning of war | War words AND war begin words |
| Acts: 7. Escalation of war | Actors words AND actors fight words |
| Acts: 8. Terrorist acts | Terrorist words AND terrorism act words |
Table A.1: Search Query for CGPR Index Based on Earnings-Call Transcripts - Panel B. Search Words
| Phrases | |
|---|---|
| Topic sets: War words | war OR conflict OR hostilities OR revolution* OR insurrection OR uprising OR revolt OR coup OR geopolitical |
| Topic sets: Peace words | peace OR truce OR armistice OR treaty OR parley |
| Topic sets: Military words | military OR troops OR missile* OR "arms" OR weapon* OR bomb* OR warhead* |
| Topic sets: Nuclear bigrams | "nuclear war*" OR "atomic war*" OR "nuclear missile*" OR "nuclear bomb*" OR "atomic bomb*" OR "h-bomb*" OR "hydrogen bomb*" OR "nuclear test" OR "nuclear weapon*" |
| Topic sets: Terrorism words | terror* OR guerrilla* OR hostage* |
| Topic sets: Actors words | allies* OR enemy* OR insurgent* OR foe* OR army OR navy OR aerial OR troops OR rebels |
| Threat/act sets: Threat words | threat* OR warn* OR fear* OR risk* OR concern* OR danger* OR doubt* OR crisis OR trouble* OR dispute* OR tension* OR imminent* OR inevitable OR footing OR menace* OR brink OR scare OR peril* |
| Threat/act sets: Peace disruption words | threat* OR menace* OR reject* OR peril* OR boycott* OR disrupt* |
| Threat/act sets: Buildup words | buildup* OR build-up* OR sanction* OR blockade* OR embargo OR quarantine OR ultimatum OR mobilize* |
| Threat/act sets: War begin words | begin* OR start* OR declare* OR begun OR began OR outbreak OR "broke out" OR breakout OR proclamation OR launch* |
| Threat/act sets: Actor fight words | advance* OR attack* OR strike* OR drive* OR shell* OR offensive OR invasion OR invade* OR clash* OR raid* OR launch* |
| Threat/act sets: Terrorism act words | attack OR act OR bomb* OR kill* OR strike* OR hijack* |
Table A.1: Search Query for CGPR Index Based on Earnings-Call Transcripts - Panel C. Excluded words
| Exclusion words | movie* OR film* OR museum* OR anniversary* OR obituary* OR memorial* OR arts OR book OR books OR memoir* OR "price war" OR game OR story OR history OR veteran* OR tribute* OR sport OR music OR racing OR cancer OR "real estate" OR mafia OR trial OR tax |
|---|
Note: This table lists the search query used to construct the country-specific geopolitical risk index based on earnings-call transcripts ($$CGPR^{T}$$). Thequery is based on Caldara and Iacoviello, 2022's with slight modification. The truncation character (*) denotes a search including all possible endings of a word, (e.g., "threat*" includes "threat" or "threats" or "threatening").
Figure A.1: Country-specific Geopolitical Risk and Other Risk Indices
Figure A.2: Bank-specific Geopolitical Risk Indices
This section presents additional regression results and supporting evidence that complement the main findings in the paper.
Appendix Table B.1 reports results from Equation (5), using $$CGPR^{T}$$ as the main regressor. Similarly to the results with $$CGPR^{N}$$ in Table 2, banks reduce cross-border exposures to countries experiencing rising geopolitical risk (Columns (3) and (4)), while their operations through local offices in those countries remain largely unchanged (Columns (5) and (6)).
Appendix Table B.2 reports regressions based on Equation (5), where the dependent variable is either banks' local claims in foreign currency (primarily U.S. dollars) (Columns (1) and (2)) or in local currency (Columns (3) and (4)). The results show no significant response of foreign currency claims to geopolitical risk, while local currency-denominated claims exhibit some decline. When geopolitical risk rises, the local currency typically depreciates, reducing the U.S. dollar value of local currency claims without necessarily affecting the underlying local operations. Thus, the observed decline is likely driven by exchange rate effects.
Appendix Figure B.3 shows the evolution of cross-border and local claims on Russia following three major geopolitical events: the conflict with Georgia in 2008:Q3, the annexation of Crimea in 2013:Q4, and the invasion of Ukraine in 2022:Q1. Panel (a) presents claims by the U.S. banking sector, while Panel (b) shows claims by all BIS-reporting banking sectors. In both cases, cross-border and local claims declined after each shock, but the decline in local exposures was significantly smaller than cross-border exposures.
Appendix Tables B.3 and B.4 parallel Panels (a) and (b) of Table 4, replacing log local claims with a bank's share of local liabilities in total assets as the key interaction variable. Specifically, $$CGPR^N_t$$ and $$CGPR^N_{t-1}$$ are interacted with local liability shares at time $$t-1$$ and $$t-2$$, respectively. The results are qualitatively similar. Appendix Table B.3 shows that banks with higher local funding shares are less likely to reduce overall foreign exposures in response to geopolitical risk. This effect is driven bylocal exposures (Columns (3) and (4)), while the coefficients for cross-border exposures (Columns (5) and (6)) are statistically indistinguishable from zero. By contrast, Appendix Table B.4 shows that local funding shares do not significantly affect how banks adjust foreign exposures in response to other types of risk, including the country risk index (CRI) by Hassan et al., 2023, the World Uncertainty Index (WUI) by Ahir et al., 2022, and sovereign CDS spreads. These resultsreinforce those in Table 4 and further support the model's Proposition 1(c).
Appendix Table B.5 parallels Table 6, using $$CGPR^T$$ instead of $$CGPR^N$$ to construct the $$BGPR$$ measures in Equation (5.2). Panel (a) reports loan-level results; Panel (b), bank-level results. As in Table 6, the coefficients on $$BGPR^T\boldsymbol{1}($$Local$$)$$ are negative and significant, indicating that geopolitical risk transmits to domestic credit supply through banks' local foreign exposures. By contrast, the coefficients on $$BGPR^T\boldsymbol{1}($$Cross-border$$)$$ are not statistically significant, suggesting that spillovers operate primarily through affiliates. When both indices are included, the local interaction remains significant. The results are consistent with the model's prediction that cross-border claims are more easily liquidated, freeing lending capacity, while affiliate exposures tighten capital constraints.
Appendix Table B.6 presents bank-level estimates of the impact of geopolitical risk--measured using subindices of $$BGPR^{T}$$ that distinguish between threats and acts--on U.S. banks' loan origination to domestic firms using the FR Y-14 data. Consistent with Panel (b) of Table 7, which reports loan-level results, perceived threats of geopoliticalrisk have a stronger effect on loan origination decisions than realized shocks.
Appendix Table B.6 presents regression results on the spillover effects of geopolitical risk--measured using subindices of $$BGPR^{T}$$--on U.S. banks' domestic C&I lending standards, using SLOOS data. Similarly to the findings on loan origination, perceived threats of geopolitical risk have a stronger influence on lending standards than realized events.
Appendix Table B.8 reports regression results based on Equation (12), where the dependent variable captures banks' responses to whether they tightened or loosened lending standards for commercial real estate (CRE) loans. The results show that banks also tighten CRE lending standards in response to geopolitical risk, reinforcing the finding that foreign geopolitical shocks lead to a contraction in domestic credit supply. Notably, the U.S. CRE sector is less directly exposed to geopolitical risk than sectors like trade and manufacturing. Therefore, the observed spillovers are unlikely to be driven by credit demand responses.
Table B.1: Response of Banks' Foreign Operations to Geopolitical Risk, $$CGPR^T$$
| $$ln(exp_{bct})$$ | Total (1) | Total (2) | Cross-border (3) | Cross-border (4) | Local (5) | Local (6) |
|---|---|---|---|---|---|---|
| $$CGPR^T_{ct}$$ | -0.016*, (0.009) | -0.016*, (0.009) | -0.023**, (0.012) | -0.023**, (0.011) | -0.015, (0.018) | -0.014, (0.018) |
| $$CGPR^T_{ct-1}$$ | -0.000, (0.009) | -0.001, (0.008) | -0.004, (0.011) | -0.004, (0.010) | -0.010, (0.026) | -0.011, (0.026) |
| $$\boldsymbol{1}$$ $$(Sanction)_{t}$$ | -0.120***, (0.031) | -0.140***, (0.034) | -0.246***, (0.052) | |||
| $$ln(Exch. Rate)_{t}$$ | -0.009, (0.008) | -0.004, (0.011) | -0.163***, (0.056) | |||
| $$ln(Stock Index)_{t}$$ | 0.081, (0.071) | 0.155*, (0.081) | 0.200, (0.161) | |||
| $$ln(Exch. Rate)_{t-1}$$ | 0.009, (0.009) | 0.011, (0.012) | -0.016, (0.058) | |||
| $$ln(Stock Index)_{t-1}$$ | -0.120*, (0.063) | -0.176**, (0.072) | -0.198, (0.154) | |||
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 35515 | 33501 | 34813 | 32826 | 11587 | 11094 |
| $$R^{2}$$ | 0.947 | 0.949 | 0.936 | 0.937 | 0.938 | 0.942 |
Note: This table reports results from regressions at the bank-country-time level based on Equation (5) using the FFEIC 009 data covering the sample period 2013:Q1 through 2022:Q4. $$CGPR^{T}$$ denotes the country-specific geopolitical risk index constructed based on earnings-call transcripts using the NL Analytics platform. The dependent variable is the log total foreign claims in Columns (1) through (3), log cross-border claims in Columns (4) through (6), and log local claims in Columns (7) through (9). Columns (1), (4), and (7) show the baseline results for each dependent variable. Columns (2), (5), and (8) add country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable that takes the value 1 if the country faces any sanctions from the United States. All regressions include bank-country and country-time fixed effects. $$CGPR^T$$ is standardized by its respective standard deviation within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.2: Response of Banks' Local Claims to Geopolitical Risk, Local versus
Foreign Currency Claims, $$CGPR^N$$
| $$ln(exp_{bct})$$ | Foreign currency (1) | Foreign currency (2) | Local currency (3) | Local currency (4) |
|---|---|---|---|---|
| $$CGPR^N_{ct}$$ | -0.027, (0.026) | -0.030, (0.028) | -0.032*, (0.018) | -0.030*, (0.017) |
| $$CGPR^N_{ct}$$ | 0.052, (0.036) | 0.047, (0.038) | -0.020, (0.017) | -0.019, (0.016) |
| $$\boldsymbol{1}$$ $$(Sanction)_{t}$$ | 0.289***, (0.061) | -0.078**, (0.039) | ||
| $$ln(Exch. Rate)_{t}$$ | -1.076***, (0.385) | -0.158***, (0.056) | ||
| $$ln(Stock Index)_{t}$$ | -0.067, (0.240) | 0.031, (0.148) | ||
| $$ln(Exch. Rate)_{t-1}$$ | 0.625, (0.392) | -0.028, (0.055) | ||
| $$ln(Stock Index)_{t-1}$$ | 0.035, (0.234) | -0.060, (0.145) | ||
| Bank-country FE | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes |
| Observations | 8038 | 7709 | 18947 | 18059 |
| $$R^{2}$$ | 0.887 | 0.888 | 0.903 | 0.907 |
Note: This table reports results from regressions at the bank-country-time level based on Equation (5) using the FFEIC 009 data for the sample period 1986:Q1 through 2022:Q4. $$CGPR^N$$ denotes the(recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022. The dependent variable is the log local claims in foreign currency in Columns (1) and (2) and log local claims in local currency in Columns (3) and (4). Columns (1) and (3) show the baseline results for each dependent variable. Columns (2) and (4) add country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States. All regressions include bank-country and country-time fixed effects. $$CGPR^N$$ is standardized by its respective standard deviation within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Figure B.3: Banks' Cross-border and Local Exposures to Russia
Table B.3: Response of Banks' Foreign Operations to Geopolitical Risk, by Ex Ante Local Liability Share
| $$ln(exp_{bct})$$ | Total Exp. (1) | Total Exp. (2) | Local (3) | Local (4) | Cross-border (5) | Cross-border (6) |
|---|---|---|---|---|---|---|
| $$CGPR^N_{ct}$$ | -0.018**, (0.009) | -0.021**, (0.010) | 0.003, (0.015) | 0.001, (0.016) | -0.027***, (0.010) | -0.030***, (0.010) |
| $$CGPR^N_{ct}$$ $$\times$$ $$LL^{Shr}_{bct-1}$$ | 0.003, (0.005) | 0.001, (0.005) | 0.013, (0.011) | 0.015, (0.011) | -0.013, (0.009) | -0.013, (0.009) |
| $$CGPR^N_{ct-1}$$ | -0.014 | -0.019* | 0.004 | 0.001 | -0.019* | -0.023** |
| (0.009) | (0.010) | (0.014) | (0.015) | (0.010) | (0.012) | |
| $$CGPR^N_{ct-1}$$ $$\times$$ $$LL^{Shr}_{bct-2}$$ | 0.015***, (0.006) | 0.014***, (0.006) | 0.026**, (0.012) | 0.027**, (0.012) | -0.005, (0.008) | -0.004, (0.009) |
| $$LL^{Shr}_{bct-1}$$ | -0.014**, (0.007) | -0.016**, (0.007) | -0.021, (0.013) | -0.024*, (0.013) | -0.022**, (0.010) | -0.022**, (0.011) |
| $$LL^{Shr}_{bct-2}$$ | 0.017***, (0.007) | 0.016**, (0.007) | 0.032**, (0.016) | 0.037**, (0.016) | 0.010, (0.010) | 0.009, (0.010) |
| Macro Controls | No | Yes | No | Yes | No | Yes |
| Bank-country | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 94336 | 77649 | 30303 | 27420 | 93173 | 76556 |
| $$R^{2}$$ | 0.911 | 0.919 | 0.886 | 0.894 | 0.891 | 0.900 |
Note: This table reports results from regressions at the bank-country-time level based on an augmented version of Equation (5) using the FFEIC 009 data for the sample period 1986:Q1 through 2022:Q4. $$CGPR^N$$ denotes the (recent) country-specific geopolitical risk index from Caldara and Iacoviello, 2022. $$LL^{Shr}_{bct-1}$$ denotes the local liabilities for bank $$b$$ from country $$c$$ as a share of its total lending to that country, calculated as a four-quarter moving average from $$t-4$$ to $$t-1$$. The dependent variable is the log total foreign claims in Columns (1) and (2), log local claims in Columns (3) and (4), and log cross-border claims in Columns (5) and (6). Columns (1), (3), and (5) show the baseline results for each dependent variable. Columns (2), (4), and (6) add country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States. All regressions include bank-country and country-time fixed effects. $$CGPR^N$$ is standardized by its respective standard deviation within the sample. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.4: Other Risks and Banks' Foreign Operations, by Ex Ante Local Liability Position
| CRI (1) Tot | CRI (2) LC | CRI (3) XB | WUI (4) Tot | WUI (5) LC | WUI (6) XB | CDS (7) Tot | CDS (7) LC | CDS (7) XB | |
|---|---|---|---|---|---|---|---|---|---|
| $$CRI_t$$ | 0.000, (0.014) | 0.022, (0.018) | -0.014, (0.016) | ||||||
| $$CRI_t$$ $$\times$$ $$LL^{Shr}_{bct-1}$$ | 0.004, (0.007) | -0.003, (0.012) | 0.002, (0.008) | ||||||
| $$CRI_{t-1}$$ | -0.001, (0.014) | 0.029, (0.019) | -0.006, (0.015) | ||||||
| $$CRI_{t-1}$$ $$\times$$ $$LL^{Shr}_{bct-2}$$ | 0.011, (0.007) | 0.016, (0.014) | 0.005, (0.007) | ||||||
| $$WUI_t$$ | 0.010**, (0.005) | 0.003, (0.008) | 0.007, (0.005) | ||||||
| $$WUI_t$$ $$\times$$ $$LL^{Shr}_{bct-1}$$ | -0.002, (0.002) | 0.007, (0.006) | -0.006**, (0.003) | ||||||
| $$WUI_{t-1}$$ | -0.001, (0.005) | 0.004, (0.008) | -0.004, (0.005) | ||||||
| $$WUI_{t-1}$$ $$\times$$ $$LL^{Shr}_{bct-2}$$ | 0.005**, (0.002) | 0.002, (0.006) | 0.001, (0.003) | ||||||
| $$ln(CDS)_t$$ | 0.030, (0.041) | -0.015, (0.049) | 0.035, (0.051) | ||||||
| $$ln(CDS)_t$$ $$\times$$ $$LL^{Shr}_{bct-1}$$ | 0.008, (0.011) | 0.027*, (0.015) | -0.003, (0.012) | ||||||
| $$ln(CDS)_{t-1}$$ | 0.015, (0.038) | -0.197***, (0.049) | 0.048, (0.046) | ||||||
| $$ln(CDS)_{t-1}$$ $$\times$$ $$LL^{Shr}_{bct-2}$$ | -0.003, (0.010) | -0.002, (0.016) | -0.000, (0.011) | ||||||
| $$LL^{Shr}_{bct-1}$$ | -0.016, (0.010) | -0.020*, (0.012) | -0.018, (0.012) | -0.015**, (0.006) | -0.031**, (0.015) | -0.018*, (0.010) | -0.044, (0.034) | -0.108**, (0.055) | -0.010, (0.039) |
| $$LL^{Shr}_{bct-2}$$ | -0.002, (0.010) | 0.007, (0.014) | 0.006, (0.011) | 0.013*, (0.007) | 0.038**, (0.018) | 0.011, (0.009) | 0.025, (0.032) | 0.029, (0.062) | 0.013, (0.034) |
| Macro Controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-country FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Bank-time FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 13183 | 12631 | 12521 | 15171 | 14490 | 14347 | 14654 | 13982 | 13803 |
| $$R^{2}$$ | 0.960 | 0.943 | 0.922 | 0.959 | 0.940 | 0.922 | 0.961 | 0.941 | 0.922 |
Note: This table reports results from regressions at the bank-country-time level based on an augmented version of Equation (5) with alternative country-specific risk indices as the main regressor (instead of $$CGPR$$). The alternative indices include CRI by Hassan et al., 2023 (Columns (1) through (3)), WUI by Ahir et al., 2022 (Columns (4) through (6)), and log sovereign CDS spreads (Columns (7) through (9)). $$LL^{Shr}_{bct-1}$$ denotes the local liabilities for bank $$b$$ from country $$c$$ as a share of its total lending to that country, calculated as a four-quarter moving average from $$t-4$$ to $$t-1$$. The dependent variable is the log total foreign claims in Columns (1) through (3), log local claims in Columns (4) through (6), and log cross-border claims in Columns (7) through (9). All regressions include country-level macro controls, including a country's log exchange rate vis-à-vis the U.S. dollar, log domestic stock price index, and an indicator variable equal to 1 if the country faces any sanctions from the United States, as well as bank-country and country-time fixed effects. Standard errors, shown in parentheses, are clustered at the country and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.5: Geopolitical Risk Transmission: Cross-border versus Local Exposure, $$BGPR^T$$ (a) Loan Level
| $$ln(orig_{bit})$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$BGPR^T_{bt}$$ ( $$\boldsymbol{1}$$(Local)) | -0.059***, (0.020) | -0.053**, (0.021) | -0.064***, (0.020) | -0.057***, (0.020) | ||
| $$BGPR^T_{bt}$$ ( $$\boldsymbol{1}$$(Cross-border)) | -0.051, (0.347) | -0.050, (0.366) | 0.263, (0.342) | 0.228, (0.351) | ||
| Bank Controls | No | Yes | No | Yes | No | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Firm-time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 205642 | 199753 | 205642 | 199753 | 205642 | 199753 |
| $$R^{2}$$ | 0.594 | 0.592 | 0.594 | 0.592 | 0.594 | 0.592 |
Table B.5: Geopolitical Risk Transmission: Cross-border versus Local Exposure, $$BGPR^T$$ (b) Bank Level
| $$ln(orig_{bit})$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$BGPR^T_{bt}(\boldsymbol{1}$$(Local)$$)$$ | -0.035, (0.060) | -0.036, (0.060) | -0.031, (0.057) | -0.032, (0.057) | ||
| $$BGPR^T_{bt-1}(\boldsymbol{1}$$(Local)$$)$$ | -0.144**, (0.059) | -0.149**, (0.059) | -0.156***, (0.058) | -0.159***, (0.059) | ||
| $$BGPR^T_{bt}(\boldsymbol{1}$$(Cross-border)$$)$$ | -0.822, (0.868) | -0.769, (0.857) | -1.358, (0.911) | -1.309, (0.893) | ||
| $$BGPR^T_{bt-1}(\boldsymbol{1}$$(Cross-border)$$)$$ | 0.565, (0.776) | 0.616, (0.780) | 0.944, (0.880) | 1.015, (0.871) | ||
| Bank Controls | No | Yes | No | Yes | No | Yes |
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 475 | 475 | 475 | 475 | 475 | 475 |
| $$R^{2}$$ | 0.955 | 0.956 | 0.952 | 0.952 | 0.955 | 0.956 |
Note: This table reports results from regressions with log loan origination amount ($$orig$$) as the dependent variable using data from FRY-14 for the sample period 2013:Q1 through 2022:Q4. Panel (a) reports results from regressions at the loan level based on Equation (9), using a modified $$BGPR$$ constructed using Equation (5.2). Panel (b) reports results from regressions at the bank-time level based on Equation (10). Bank controls include lagged Tier 1 capital ratio and liquid-asset ratio. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.6: Geopolitical Risk and Domestic Loan Origination: Threat vs. Act (Bank Level)
| $$orig_{bt}$$ | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| $$BGPR^{T(Threat)}_{bt}$$ | -0.049, (0.069) | |||
| $$BGPR^{T(Threat)}_{bt-1}$$ | -0.171**, (0.069) | |||
| $$BGPR^{T(Act)}_{bt}$$ | 0.012, (0.038) | |||
| $$BGPR^{T(Act)}_{bt-1}$$ | -0.045, (0.039) | |||
| $$BGPR^{T^{fin}(Threat)}_{bt}$$ | -0.069, (0.067) | |||
| $$BGPR^{T^{fin}(Threat)}_{bt-1}$$ | -0.150**, (0.067) | |||
| $$BGPR^{T^{fin}(Act)}_{bt}$$ | -0.025, (0.035) | |||
| $$BGPR^{T^{fin}(Act)}_{bt-1}$$ | -0.035, (0.033) | |||
| Bank Controls | Yes | Yes | Yes | Yes |
| Bank FE | Yes | Yes | Yes | Yes |
| Time FE | Yes | Yes | Yes | Yes |
| Observations | 475 | 475 | 475 | 475 |
| $$R^{2}$$ | 0.956 | 0.952 | 0.956 | 0.953 |
Note: This table reports results from bank-level regressions with log loan origination amount ($$orig$$) as the dependent variable using data from FR Y-14for the sample period 2013:Q1 through 2022:Q4 based on Equation (10). The main regressors are subindices of $$BGPR^T$$, or bank-specific geopolitical risk index based on $$CGPR^{T}$$, which is constructed with earnings-call transcripts using the NL Analytics platform and captures geopolitical risk perceptions by firms worldwide. $$BGPR^{T(Threat)}$$ captures firms' perceptions of geopolitical risk threats, and $$BGPR^{T(Act)}$$ captures their perceptions of geopolitical risk stemming from acts. $$BGPR^{T^{fin}(Threat)}$$ and $$BGPR^{T^{fin}(Act)}$$ capture financial firms' perceptions of geopolitical risk stemming from threats and acts, respectively. Bank controls include lagged Tier 1 capital ratio and liquid-asset ratio. All the geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.7: Geopolitical Risk and Lending Standards, Threats versus Acts
| $$ls_{bt}$$ | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| $$\Delta \log(BGPR^{T (Threat)}_{bt})$$ | -0.036***, (0.012) | |||
| $$\Delta \log(BGPR^{T (Threat)}_{bt-1})$$ | -0.011, (0.010) | |||
| $$\Delta \log(BGPR^{T (Act)}_{bt})$$ | -0.002, (0.013) | |||
| $$\Delta \log(BGPR^{T (Act)}_{bt-1})$$ | 0.011, (0.012) | |||
| $$\Delta \log(BGPR^{T^fin(Threat)}_{bt})$$ | -0.025**, (0.011) | |||
| $$\Delta \log(BGPR^{T^fin(Threat)}_{bt-1})$$ | -0.013, (0.011) | |||
| $$\Delta \log(BGPR^{T^fin(Act)}_{bt})$$ | -0.101, (0.089) | |||
| $$\Delta \log(BGPR^{T^fin(Act)}_{bt-1})$$ | 0.056, (0.065) | |||
| Bank FE | Yes | Yes | Yes | Yes |
| Macro Controls | Yes | Yes | Yes | Yes |
| Bank Controls | Yes | Yes | Yes | Yes |
| Observations | 1466 | 1211 | 1430 | 144 |
| $$R^{2}$$ | 0.353 | 0.369 | 0.347 | 0.450 |
Note: This table reports bank-level regression results based on Equation (13), in which the dependent variable is banks' response to the SLOOS survey question on tightening, maintaining, or loosening credit standards for C&I loans to large and medium-sized firms, using a sample spanning from 1990:Q2 through 2022:Q2. Each column corresponds to a subindex of $$BGPR^T$$ as the main regressor, where $$BGPR^T$$ is bank-specific geopolitical risk index based on $$CGPR^{T}$$, which is constructed with earnings-call transcripts using the NL Analytics platform and captures geopolitical risk perceptions by firms worldwide. $$BGPR^{T(Threat)}$$ captures firms' perceptions of geopolitical risk threats, and $$BGPR^{T(Act)}$$ captures their perceptions of geopolitical risk stemming from acts. Similarly, $$BGPR^{T^{fin}(Threat)}$$ and $$BGPR^{T^{fin}(Act)}$$ represent the corresponding subcomponents for threats and acts, respectively, when the firm sample is restricted to financial firms. All specifications include bank fixed effects, macroeconomic controls, bank-level controls, and the lagged dependent variable. The geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
Table B.8: Geopolitical Risk and Lending Standards on Commercial Real Estate Loans
| $$ls_{bt}$$ | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| $$\Delta \log(BGPR^N_{bt})$$ | -0.002, (0.017) | 0.000, (0.017) | -0.001, (0.017) | |||
| $$\Delta \log(BGPR^N_{bt-1})$$ | -0.045***, (0.017) | -0.040***, (0.016) | -0.040***, (0.016) | |||
| $$\Delta \log(BGPR^T_{bt})$$ | -0.026, (0.020) | -0.041*, (0.021) | -0.038*, (0.020) | |||
| $$\Delta \log(BGPR^T_{bt-1})$$ | -0.043**, (0.017) | -0.046***, (0.017) | -0.042**, (0.017) | |||
| Bank FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Macro Controls | No | Yes | Yes | No | Yes | Yes |
| Bank Controls | No | No | Yes | No | No | Yes |
| Observations | 1156 | 1156 | 1152 | 704 | 704 | 704 |
| $$R^{2}$$ | 0.246 | 0.298 | 0.325 | 0.250 | 0.305 | 0.357 |
Note: This table reports bank-level regression results based on Equation (12), in which the dependent variable is banks' response to the SLOOS survey question on tightening, maintaining, or loosening credit standards for commercial real estates loans. The main regressor $$BGPR^{N}$$ (Columns (1) through (3)) is the bank-specific geopolitical risk index constructed from $$CGPR^{N}$$, the country-specific geopolitical risk index from Caldara and Iacoviello (2022), using Equation (1). $$BGPR^{T}$$ (Columns (4) through (6)) is the
bank-specific geopolitical risk index derived from $$CGPR^{T}$$ , which captures firms geopolitical risk perceptions based on earnings-call transcripts processed through the NL Analytics platform. Columns (2) and (5) add macroeconomic controls, including (log) changes in the two-year Treasury yield, the yield curve slope (10y-2y), the CBOE Volatility Index (VIX), the S&P 500 index, and U.S. industrial production. Columns (3) and (6) further control for loan demand, as well as banks liquid-asset and Tier 1 capital ratios. The geopolitical risk indices are standardized by their respective standard deviations within the sample. Standard errors, shown in parentheses, are clustered at the bank and time level. *$$p<.1$$; **$$p<.05$$; ***$$p<.01$$.
This section contains the proofs of the propositions stated in Section 4.
Proposition 1:
| $$\displaystyle \frac{\partial (\pi_2^{A, C}-\pi_2^{X, C})}{\partial p}=-i D^*_2<0.$$ | (C.1) |
Because $$\pi_2^{A, C}-\pi_2^{X, C}$$ decreases in $$p$$, $$\Delta \hat{\delta}$$ decreases in $$p$$.
| $$\displaystyle \frac{\partial (\pi_2^{A, C}-\pi_2^{X, C})}{\partial D^*_2}=i (1-p) >0.$$ | (C.2) |
Because $$\pi_2^{A, C}-\pi_2^{X, C}$$ increases in $$D^*_2$$, $$\Delta \hat{\delta}$$ increases in $$D^*_2$$.
| $$\displaystyle L_2^{G,C}=\frac{L^*+R_1L_1- D_1 i-\mu L^* \alpha(p^G)}{\mu}> L_2^{B,C}=\frac{L^*+R_1L_1- D_1 i-\mu L^* \alpha(p^B)}{\mu}$$ | (C.3) |
| $$\displaystyle L^L_2=\frac{\delta L^*+R_1L_1 - D_1 i}{\mu}>L_2^{B,C}=\frac{L^*+R_1L_1- D_1 i-\mu L^* \alpha(p^B)}{\mu}.$$ | (C.4) |
| $$\displaystyle L_1=\frac{E_1-\mu L^* \alpha(\phi, p^G, p^B)}{\mu}>L_2^{B,C}=\frac{L^*+R_1L_1- D_1 i-\mu L^* \alpha(p^B)}{\mu}.$$ | (C.5) |
Rearranging delivers:
| $$\displaystyle \frac{(R_1-1) L_1-(i-1) D_1}{\mu}<(\alpha(p^B)-\alpha(\phi, p^B, p^G)) L^*.$$ | (C.6) |
Because $$\alpha(p^G)-\alpha(\phi, p^B, p^G)<0$$ and $$\frac{(R_1-1) L_1-(i-1) D_1}{\mu}>0$$, $$L_2^{G,C}>L_1$$.
We outline the parameter assumptions needed for a model solution in which the bank optimally invests both domestically and internationally at $$t=0$$ and, when geopolitical risk is high at $$t=1$$, liquidates its cross-border investment but retains its affiliate lending. Profits with liquidation at $$t=2$$ are given by:
| $$\displaystyle \pi_2^{L}=(\frac{R-i}{\mu}+i) \left((\frac{R-i}{\mu}+i) E_1+((\delta -i)-(R-i) \alpha(\phi, p^G, p^B))L^* \right)<\pi_2^D.$$ | (C.7) |
Second-period profits without liquidation under the cross-border mode are given by:
| $$\displaystyle \pi_2^{X, C}=p R^* L^*+L^C_2 R-D^C_2 i.$$ | (C.8) |
Plugging in $$L^{C}_2=\frac{E^C_2}{\mu}-L^* \alpha(p)$$, $$D^{C}_2=L^C_2-(L_1 R-D_1 i)$$ and $$E^{C}_2=E_1+(R-1) L_1+(1-i)D_1$$, we obtain:
| $$\displaystyle \pi_2^{X, C}=p R^* L^*+ (R-i) L_1 \left(\frac{R-i}{\mu}+i\right)+(R-i) L^* (\frac{1}{\mu} (1-i) -\alpha(p))-L^* i^2+ \left(\frac{R-i}{\mu}+i\right) E_1 i.$$ | (C.9) |
Second-period profits without liquidation under the affiliate mode are given by:
| $$\displaystyle \pi_2^{A, C}=p R^* L^*+ (R-i) L_1 \left(\frac{R-i}{\mu}+i\right)+(R-i) L^* (\frac{1}{\mu} (1-i) -\alpha(p))-L^* i^2+ \left(\frac{R-i}{\mu}+i\right) E_1 i \ + (1-p) D^*_2 i.$$ | (C.10) |
By setting $$\pi_2^{X, C}=\pi_2^{L}$$ and $$\pi_2^{A, C}=\pi_2^{L}$$, we can get $$\hat{\delta^{X}}$$ and $$\hat{\delta^{A}}$$.
| $$\displaystyle \hat{\delta^{X}}=\frac{-R\alpha \mu+R+R^* \mu p+i \alpha(p)\mu-i}{R+i \mu-i }.$$ | (C.11) |
From $$\pi_2^{A, C}=\pi_2^{L}$$, we obtain:
| $$\displaystyle \hat{\delta^{A}}=\frac{-R\alpha \mu+R+R^* \mu p+i \alpha(p)\mu-i+(1-p) \frac{\mu}{L^*} D^*_2 i}{R+i \mu-i }.$$ | (C.12) |
Assume that $$\min\{\hat{\delta^{A, B}},\hat{\delta^{X, G}}, 1\} >\delta>\hat{\delta^{X, B}}$$. Then the bank does not liquidate the foreign investment in the good state of the world, while the bank liquidates the foreign investment under the cross-border mode in the bad state of the world but not under the affiliate mode.
At $$t=0$$, banks chose the investment that maximizes their expected (second-period) profits. The domestic asset invested for two periods delivers the following profits:
| $$\displaystyle \pi^D=\left(\frac{R-i}{\mu}+i\right)^2 E_1$$ | (C.13) |
Assuming $$\hat{\delta^{A, B}}>\delta>\hat{\delta^{X, B}}$$ and $$\hat{\delta^{A, G}}>\hat{\delta^{X, G}}>\delta$$, expected profits under cross-border investment are:
| $$\displaystyle \pi^X= (1-\phi) \pi_2^{X, C, G}+\phi \pi_2^{L}.$$ | (C.14) |
And profits with a foreign affiliates are:
| $$\displaystyle \pi^A= (1-\phi) \pi_2^{A, C, G}+\phi \pi_2^{A, C, B}-\kappa.$$ | (C.15) |
Since $$\pi^{L}<\pi^D$$ even for $$\delta=1$$, $$\pi^D<\pi^X$$ implies $$\pi_2^{X, C, G}>\pi^L$$, hence $$\hat{\delta^{X, G}}>1$$. In other words, if investing both at home and abroad yields a higher expected return than investing solely in the domestic asset, and given that $$\delta <1$$, the cross-border investment is never liquidated in the good state. Furthermore, since $$\hat{\delta^{X, G}}<\hat{\delta^{A, G}}$$, the same holds for the affiliate mode in the good state.
In addition to the assumptions on $$\delta$$, we therefore require parameters such that $$\pi^D<\pi^X$$, meaning that $$\pi_2^{X, C, G}$$ needs to be sufficiently high, since $$\pi_2^{X, C, B}<\pi_2^L$$ follows from the assumption on $$\delta$$. This condition can be achieved by setting $$(1-\phi) p^G R^*$$--the expected return in the good state of the world --sufficiently high. If $$\kappa=0$$, we know that $$\pi^A>\pi^X$$. Hence, we additionally require $$\kappa$$ to be sufficiently small to satisfy $$\pi^D<\pi^A$$.