Figure 1: U.S. Banks' Foreign Operations
Panel A: Foreign Exposures as a Share of Total Assets This line graph shows the ratio of foreign exposures to total assets by U.S. banks from 1990:Q1 to 2021:Q4. The y-axis ranges from 0 to 0.4, representing the share of foreign lending. The line showing the share hovers around 0.2 with a notable increase after the Great Financial Crisis around 2009 ending at a value of around 0.22 for the most recent quarter.
Panel B: Local Exposures as a Share of Foreign Exposures This graph shows the ratio of affiliate (local) foreign lending to total foreign exposures. The y-axis ranges from 0.0 to 0.8, while the x-axis spans 1990:Q1 to 2021:Q4. The ratio hovers roughly around 0.45 with a small increase around 2004 that reverts around 2012.
Panel C: Distribution of Foreign Exposure by Region, 2010:Q4 This graph shows a kernel density plot. The y-axis ranges from 0 to 6, while the x-axis ranges from 0 to 1. The graph shows four curves for Europe, Asia, Latin America, and Rest of World. The curve for Europe is fairly flat, starting at around 1.6 and going down continuously to zero from x=0.5 to x=1. The curves for Asia and Latin America have their mass between 0 and 0.1 where the y-axis reaches a value of 4. The curve for Asia hits 2 at x=0.1 and is close to 0 starting from x=0.4. The curve for Latin America behaves similarly, except that it exhibits a second small bump from x=0.4 to x=0.7. The curve for the Rest of World starts at around 2 and then falls slowly and continuously, reaching zero at around x=0.8.
Panel D: Distribution of Foreign Claims by Country for Selected Bank, 2019:Q4 This graph shows a kernel density plot. The y-axis ranges from 0 to 6, while the x-axis ranges from 0 to 1. The graph shows four curves for Europe, Asia, Latin America, and Rest of World. The curve for Europe is fairly flat, starting at around 1.8 and going down continuously to zero from x=0.4 to x=0.8. The curves for Asia and Rest of World have their mass between 0 and 0.1 where the y-axis reaches a value of 3. The curve for Latin America exceeds the value of 6 close to x=0 and then falls abruptly to around 1.8 at x=0.1. The curve then falls further, hovering at a small number ending at x=0.8.
Panel E Distribution of Foreign Claims by Country for Selected Banks, 2010:Q4
| Date | Bank name | Country | Country claims as a percent of the bank's total assets |
|---|---|---|---|
| 12/31/2010 | Bank of NY Mellon | Australia | 1.18 |
| 12/31/2010 | Bank of NY Mellon | France | 2.53 |
| 12/31/2010 | Bank of NY Mellon | Germany | 3.79 |
| 12/31/2010 | Bank of NY Mellon | Japan | 1.33 |
| 12/31/2010 | Bank of NY Mellon | Netherlands | 2.33 |
| 12/31/2010 | Citigroup | France | 1.48 |
| 12/31/2010 | Citigroup | Germany | 0.94 |
| 12/31/2010 | Citigroup | India | 1.48 |
| 12/31/2010 | Goldman Sachs | China | 1.62 |
| 12/31/2010 | Goldman Sachs | France | 4.58 |
| 12/31/2010 | Goldman Sachs | Germany | 2.54 |
| 12/31/2010 | Goldman Sachs | Japan | 4.24 |
| 12/31/2010 | Goldman Sachs | United Kingdom | 1.32 |
| 12/31/2010 | State Street | Australia | 2.55 |
| 12/31/2010 | State Street | Netherlands | 1.62 |
| 12/31/2010 | State Street | United Kingdom | 2.76 |
Panel F Distribution of Foreign Claims by Country for Selected Banks, 2019:Q4
| Date | Bank name | Country | Country claims as a percent of the bank's total assets |
|---|---|---|---|
| 12/31/2019 | Bank of NY Mellon | Belgium | 1.69 |
| 12/31/2019 | Bank of NY Mellon | Canada | 1.32 |
| 12/31/2019 | Bank of NY Mellon | Germany | 5.40 |
| 12/31/2019 | Bank of NY Mellon | Japan | 7.89 |
| 12/31/2019 | Bank of NY Mellon | United Kingdom | 4.62 |
| 12/31/2019 | Citigroup | Germany | 2.23 |
| 12/31/2019 | Citigroup | Japan | 3.98 |
| 12/31/2019 | Citigroup | Mexico | 3.69 |
| 12/31/2019 | Citigroup | South Korea | 2.13 |
| 12/31/2019 | Citigroup | United Kingdom | 3.54 |
| 12/31/2019 | Goldman Sachs | Canada | 1.86 |
| 12/31/2019 | Goldman Sachs | France | 1.93 |
| 12/31/2019 | Goldman Sachs | Germany | 3.35 |
| 12/31/2019 | Goldman Sachs | Japan | 8.42 |
| 12/31/2019 | Goldman Sachs | United Kingdom | 6.30 |
| 12/31/2019 | State Street | Australia | 1.37 |
| 12/31/2019 | State Street | Canada | 1.32 |
| 12/31/2019 | State Street | Germany | 7.21 |
| 12/31/2019 | State Street | Japan | 4.60 |
| 12/31/2019 | State Street | United Kingdom | 5.71 |
Note: Panel (a) of the figure shows U.S. banks' average foreign exposures as a share of total assets from 1990:Q1 to 2021:Q4. Panel (b) shows U.S. banks' local exposures, or exposures through foreign offices, as a share of their total foreign exposures. Panels (c) and (d) illustrate the kernel density of the share of foreign operations in four regions|Europe, Asia, Latin America, and the rest of the world|in 2010:Q4 and 2019:Q4, respectively, across U.S. banks. Panel (e) and (f) illustrate the top countries by foreign claims size (expressed as a share of total assets) in 2010:Q4 and 2019:Q4, respectively, for four selected U.S. banks. Data source(s): FFIEC 009, FR Y9-C, and Call Reports for Panels (a){(d); public version of FFIEC 009/009a for Panels (e){(f).
| ID | id_rssd | d_dt | tstmp_ext | tstmp_load | cntry_cd | xborder_claims | local_claims_post_2013 | local_claims_pre_2013 | sub_name | topholder_id | topholder_name | cntry_nm | total_assets | local_claims | total_claims | claims_as_pct_total_assets | rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 35301 | 12/31/2010 | 20110219.01 | 20110219.01 | 12106 | 2555 | 0 | 19 | STATE STREET B&TC | 1111435 | "State Street" | Netherlands | 158890.975 | 19 | 2574 | 1.619978731 | 3 |
| 2 | 35301 | 12/31/2010 | 20110219.01 | 20110219.01 | 13005 | 4386 | 0 | 0 | STATE STREET B&TC | 1111435 | "State Street" | United Kingdom | 158890.975 | 0 | 4386 | 2.760383338 | 1 |
| 3 | 35301 | 12/31/2010 | 20110219.01 | 20110219.01 | 60089 | 3610 | 0 | 444 | STATE STREET B&TC | 1111435 | "State Street" | Australia | 158890.975 | 444 | 4054 | 2.551435033 | 2 |
| 9 | 541101 | 12/31/2010 | 20110219.01 | 20110219.01 | 10804 | 6249 | 0 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | France | 247159 | 0 | 6249 | 2.528331964 | 2 |
| 10 | 541101 | 12/31/2010 | 20110219.01 | 20110219.01 | 11002 | 7333 | 0 | 2046 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Germany | 247159 | 2046 | 9379 | 3.794723235 | 1 |
| 11 | 541101 | 12/31/2010 | 20110219.01 | 20110219.01 | 12106 | 5542 | 0 | 208 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Netherlands | 247159 | 208 | 5750 | 2.326437637 | 3 |
| 12 | 541101 | 12/31/2010 | 20110219.01 | 20110219.01 | 42609 | 2261 | 0 | 1025 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Japan | 247159 | 1025 | 3286 | 1.329508535 | 4 |
| 13 | 541101 | 12/31/2010 | 20110219.01 | 20110219.01 | 60089 | 2907 | 0 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Australia | 247159 | 0 | 2907 | 1.17616595 | 5 |
| 19 | 1951350 | 12/31/2010 | 20110601.01 | 20110601.01 | 10804 | 26401 | 0 | 2015 | CITIGROUP | 1951350 | Citigroup | France | 1913902 | 2015 | 28416 | 1.484715518 | 1 |
| 20 | 1951350 | 12/31/2010 | 20110601.01 | 20110601.01 | 11002 | 17989 | 0 | 0 | CITIGROUP | 1951350 | Citigroup | Germany | 1913902 | 0 | 17989 | 0.939912284 | 3 |
| 21 | 1951350 | 12/31/2010 | 20110601.01 | 20110601.01 | 42102 | 9850 | 0 | 18549 | CITIGROUP | 1951350 | Citigroup | India | 1913902 | 18549 | 28399 | 1.483827281 | 2 |
| 27 | 2380443 | 12/31/2010 | 20111025.01 | 20111025.01 | 10804 | 41483 | 0 | 224 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | France | 911330 | 224 | 41707 | 4.576498085 | 1 |
| 28 | 2380443 | 12/31/2010 | 20111025.01 | 20111025.01 | 11002 | 23121 | 0 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | Germany | 911330 | 0 | 23121 | 2.537061218 | 3 |
| 29 | 2380443 | 12/31/2010 | 20111025.01 | 20111025.01 | 13005 | 12030 | 0 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | United Kingdom | 911330 | 0 | 12030 | 1.32004872 | 5 |
| 30 | 2380443 | 12/31/2010 | 20111025.01 | 20111025.01 | 41408 | 14481 | 0 | 243 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | China | 911330 | 243 | 14724 | 1.615660628 | 4 |
| 31 | 2380443 | 12/31/2010 | 20111025.01 | 20111025.01 | 42609 | 29932 | 0 | 8753 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | Japan | 911330 | 8753 | 38685 | 4.244894824 | 2 |
| 4 | 35301 | 12/31/2019 | 20200219.15 | 20200219.18 | 11002 | 1332 | 16367 | 0 | STATE STREET B&TC | 1111435 | "State Street" | Germany | 245610 | 16367 | 17699 | 7.206139815 | 1 |
| 5 | 35301 | 12/31/2019 | 20200219.15 | 20200219.18 | 13005 | 921 | 13110 | 0 | STATE STREET B&TC | 1111435 | "State Street" | United Kingdom | 245610 | 13110 | 14031 | 5.71271528 | 2 |
| 6 | 35301 | 12/31/2019 | 20200219.15 | 20200219.18 | 29998 | 389 | 2841 | 0 | STATE STREET B&TC | 1111435 | "State Street" | Canada | 245610 | 2841 | 3230 | 1.315093034 | 5 |
| 7 | 35301 | 12/31/2019 | 20200219.15 | 20200219.18 | 42609 | 2806 | 8488 | 0 | STATE STREET B&TC | 1111435 | "State Street" | Japan | 245610 | 8488 | 11294 | 4.598346973 | 3 |
| 8 | 35301 | 12/31/2019 | 20200219.15 | 20200219.18 | 60089 | 622 | 2744 | 0 | STATE STREET B&TC | 1111435 | "State Street" | Australia | 245610 | 2744 | 3366 | 1.370465372 | 4 |
| 14 | 541101 | 12/31/2019 | 20201209.17 | 20201209.18 | 10251 | 311 | 6140 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Belgium | 381508 | 6140 | 6451 | 1.690921291 | 4 |
| 15 | 541101 | 12/31/2019 | 20201209.17 | 20201209.18 | 11002 | 5300 | 15291 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Germany | 381508 | 15291 | 20591 | 5.397265588 | 2 |
| 16 | 541101 | 12/31/2019 | 20201209.17 | 20201209.18 | 13005 | 2306 | 15321 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | United Kingdom | 381508 | 15321 | 17627 | 4.620348721 | 3 |
| 17 | 541101 | 12/31/2019 | 20201209.17 | 20201209.18 | 29998 | 3533 | 1490 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Canada | 381508 | 1490 | 5023 | 1.316617214 | 5 |
| 18 | 541101 | 12/31/2019 | 20201209.17 | 20201209.18 | 42609 | 812 | 29298 | 0 | BANK OF NY MELLON | 3587146 | "Bank of NY Mellon" | Japan | 381508 | 29298 | 30110 | 7.892363987 | 1 |
| 22 | 1951350 | 12/31/2019 | 20200703.12 | 20200703.13 | 11002 | 27743 | 15748 | 0 | CITIGROUP | 1951350 | Citigroup | Germany | 1951158 | 15748 | 43491 | 2.228984019 | 4 |
| 23 | 1951350 | 12/31/2019 | 20200703.12 | 20200703.13 | 13005 | 14181 | 54906 | 0 | CITIGROUP | 1951350 | Citigroup | United Kingdom | 1951158 | 54906 | 69087 | 3.540820374 | 3 |
| 24 | 1951350 | 12/31/2019 | 20200703.12 | 20200703.13 | 31704 | 3723 | 68356 | 0 | CITIGROUP | 1951350 | Citigroup | Mexico | 1951158 | 68356 | 72079 | 3.694165209 | 2 |
| 25 | 1951350 | 12/31/2019 | 20200703.12 | 20200703.13 | 42609 | 31814 | 45900 | 0 | CITIGROUP | 1951350 | Citigroup | Japan | 1951158 | 45900 | 77714 | 3.982968063 | 1 |
| 26 | 1951350 | 12/31/2019 | 20200703.12 | 20200703.13 | 43001 | 8747 | 32786 | 0 | CITIGROUP | 1951350 | Citigroup | South Korea | 1951158 | 32786 | 41533 | 2.128633355 | 5 |
| 32 | 2380443 | 12/31/2019 | 20200219.12 | 20200219.13 | 10804 | 18372 | 829 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | France | 992996 | 829 | 19201 | 1.933643237 | 4 |
| 33 | 2380443 | 12/31/2019 | 20200219.12 | 20200219.13 | 11002 | 31625 | 1686 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | Germany | 992996 | 1686 | 33311 | 3.354595587 | 3 |
| 34 | 2380443 | 12/31/2019 | 20200219.12 | 20200219.13 | 13005 | 10250 | 52339 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | United Kingdom | 992996 | 52339 | 62589 | 6.303046538 | 2 |
| 35 | 2380443 | 12/31/2019 | 20200219.12 | 20200219.13 | 29998 | 17883 | 613 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | Canada | 992996 | 613 | 18496 | 1.862645972 | 5 |
| 36 | 2380443 | 12/31/2019 | 20200219.12 | 20200219.13 | 42609 | 14223 | 69341 | 0 | GOLDMAN SACHS GROUP THE | 2380443 | "Goldman Sachs" | Japan | 992996 | 69341 | 83564 | 8.415341049 | 1 |
Figure 2: Global Geopolitical Risk and Other Risk Indices
Panel A: GGPR Indices Panel A has two plots. The top graph plots the standardized global GPR index GGPRN from Caldara and Iacoviello (2022) over time. The bottom graph shows the standardized global transcript-based GPR measure GGPRT provided by this paper over time. Both indexes spike with the Iraq war in 2003:Q1, the Russia-Ukraine War in 2022:Q1 and the Israel-Hamas War in 2023:Q4. GGPRN is more volatile than GGPRT showing larger fluctuations, but otherwise the indexes look fairly similar.
Note: Panel (a) shows two global geopolitical risk (GGPR) indices, which are aggregated from country-specific geopolitical risk (CGPR) indices, covering the period from 2002:Q1 to 2023:Q4. The top chart displays GGPR from Caldara and Iacoviello (2022) (GGPRN), and the bottom chart displays GGPR constructed by applying textual analysis to earnings-call transcripts using the NL Analytics platform (GGPRT ).
Panel B: Other Risk Indices Panel B has two plots. The top graph shows Country Risk CRI from Hassan et. al (2023). The bottom panel shows the World Uncertainty Index WUI from Ahir et al. (2022). CRI exhibits spikes around the Global Financial Crisis from 2008/2009, the European Sovereign Debt Crisis in 2012, and Covid in 2020. WUI does not spike with the Global Financial Crisis but with the European Sovereign Debt Crisis, Covid and the U.S. fiscal cliff in 2012. The correlation between the two series is not very strong based on visual inspection outside of the few common spikes.
Note: Panel (b) shows the aggregated country risk index (CRI) by Hassan et al. (2023) (top), and the World Uncertainty Index (WUI) by Ahir et al. (2022) (bottom). All the indices are standardized by their respective standard deviations within the sample.
Figure 3: Geopolitical Risk and Credit Risk: Russia-Ukraine Conflicts
This graph shows the estimated delta coefficients including 95 percent confidence intervals from Equation 3. The x-axis ranges from -4 to +4, indicating quarters before and after the event with the event occurring at x=0. The y-axis ranges from -1 to 4. All coefficient estimates for t=-4 to t=0 are below zero, taking values from around -0.8 and -0.2. The magnitude of the estimated coefficients jump at t=1 crossing the y=0 line. From t=1 to t=4, the estimated coefficient value ranges from 2.5 to 3. Except for the estimated coefficient value at t=4, the confidence intervals are small.
Note: The figure illustrates the effect of geopolitical risk shocks from the Crimea con ict in 2013:Q4 and the Russia-Ukraine war in 2022:Q1 on the log average probability of default of loans to Russian borrowers relative to loans to borrowers in other countries. It plots the coefficients $$\delta_{1k}$$ from Equation (3). Standard errors, shown in parentheses, are clustered at the country- time level. Data source: FR Y-14.
Figure A.1: Country-specific Geopolitical Risk and Other Risk Indices
This figure has three panels, one for Poland, the United Kingdom, and South Korea. Each panel consists of six plots. The three plots on the left show standardized CGPRN, CGPRT and CGPRTfin over time. The three plots on the right show standardized CRI, WUI, and CDS spreads over time.
Panel A: Poland For Poland the three different GPR measures are fairly similar spiking only with the Russia-Ukraine war in 2022:Q1 and are fairly flat otherwise. CRI and WUI exhibit some volatility but no notable spikes. CDS spreads spike at the height of the Global Financial Crisis and the European Sovereign Debt Crisis in 2011:Q4.
Panel B: United Kingdom For the United Kingdom, the three GPR measures exhibit some volatility over time. CGPRN spikes with the War on Terror and the invasion of Iraq in 2003:Q1 and the terrorist bombing in London in 2005:Q3 and the Russia-Ukraine war. CGPRT spikes with the terrorist bombing and additional terror threats in 2007:Q2. The CGPRTfin measure spikes with the bombing of British targets in Turkey in 2003:Q4 and the terror threats in 2007:Q2. CRI spikes with the Global Financial Crisis and the Brexit Vote. WUI spikes with the Global Financial Crisis, the Brexit Vote, and the Ratification of Brexit. CDS spreads spike with the Global Financial Crisis, the European Sovereign Debt Crisis and the release of the highest unemployment figures since 1994 in 2011:Q4.
Panel C: South Korea For South Korea, CGPRN and CGPRT exhibit a fair amount of volatility while CGPRTfin has several spikes but also periods where the value is flat. CGPRN spikes when North Korea withdraws from the nonproliferation treaty in 2003:Q1 and around 2017:Q4 when the country launches missile and nuclear tests. CGPRT spikes additionally with a wave of political protest in 2019:Q4. CGPRTfin spikes only around 2017:Q4 when the country begins missile and nuclear tests. CRI spikes around the collapse of Lehman Brothers in 2008:Q3 and the outbreak of Covid around 2020:Q1. WUI spikes at the beginning of the Global Financial Crisis in 2007. CDS spreads spike with the collapse of Lehman Brothers. CRI and WUI are fairly volatile, while the line for CDS spreads is fairly smooth with the exception of the spike around the Lehman Brothers collapse.
Note: Panels (a), (b), and (c) illustrate the country-specific geopolitical risk (CGPR) indices and other risk indices for Poland, the United Kingdom, and South Korea, respectively, covering the period from 2002:Q1 to 2023:Q4. In each panel, the left charts, from top to bottom, display CGPR from Caldara and Iacoviello (2022) (CGPRN), CGPR constructed by applying textual analysis to earnings-call transcripts using the NL Analytics platform (CGPRT ), and a sub-index of CGPRT constructed based solely on earnings-call transcripts of financial firms (CGPRT (fin). The right charts display the country risk index (CRI) by Hassan et al. (2023) (top), the World Uncertainty Index (WUI) by Ahir et al. (2022) (middle), and the five-year CDS spread (bottom) for the respective countries. All indices are standardized by their respective standard deviations within the sample.
Figure A.2: Bank-specific Geopolitical Risk Indices
The figure has two panels. Each panel plots a bank-specific GPR index over time evaluated at different percentiles, namely at the 25th, 50th and 75th percentiles.
Panel A: $$BGPR^N$$ This panel shows the bank-specific BGPR index that is constructed based on the country-specific GPR index from Caldara and Iacoviello (2022). The x-axis ranges from 1985:Q1 to 2023:Q4. The y-axis ranges from -2 to +2. The line for the 25th percentile is very flat at a value of around -1.8. The line for the 50th percentile lies naturally between the line for the 25th and 75th percentile. The line depicting values at the 75th percentile is much more volatile compared to the other lines and takes values from below -1 to nearly 2.
Panel A: $$BGPR^T$$ This panel shows the bank-specific BGPR index that is constructed based on the country-specific GPR index derived from earnings call transcripts. The x-axis ranges from 2002:Q1 to 2023:Q4. The y-axis ranges from -1 to +3. The line for the 25th percentile is very flat at a value of around -1.6. The line for the 50th percentile lies naturally between the line for the 25th and 75th percentile but is much closer to the 25th percentile line. The line depicting values at the 75th percentile is more volatile compared to the other lines and takes values from below -1.6 to nearly 3. All lines move up with the Russia-Ukraine war in 2022:Q2.
Note: Panels (a) and (b) show the bank-specific geopolitical risk (BGPR) indices constructed based on Equation (1) using CGPRN and CGPRT , respectively, over the periods of 1985:Q1 through 2023:Q4 and 2002:Q1 through 2023:Q4. See the notes under Appendix Figure A.1 for sources and definitions of the CGPR indices. Each panel illustrates the BGPR indices at the 25th, 50th, and 75th percentile. Data sources: FFIEC 009, FR Y-9C, and Call Reports.
Figure B.3: Banks' Cross-border and Local Exposures to Russia
Note: The figure illustrates cross-border claims (blue) and local claims (red) on Russia by the U.S. banking sector in Panel (a) and all BIS-reporting banking sectors in Panel (b). The vertical lines denote three geopolitical events: Russia's conflict with Georgia in 2008:Q3, Russia's annexation of Crimea in 2013:Q4, and Russia's invasion of Ukraine in 2022:Q1. Data sources: BIS Consolidated Banking Statistics and FFIEC 009.