Forthcoming in the Journal of Money, Credit and Banking
JEL CLASSIFICATION: E44, E58, G2
KEYWORDS: credit default swap (CDS), default risk channel, LSAPs, quantitative easing, identification through heteroskedasticity
The extraordinary turmoil that roiled global financial markets during the 2007-09 crisis and the subsequently sluggish pace of economic recovery led the Federal Reserve to take a number of unprecedented steps to improve market functioning and support economic activity. In terms of both sheer scale and prominence, the attempts to stimulate the economy by purchasing large quantities of government-backed securities in the secondary market--after the target federal funds rate was lowered to its effective zero lower bound at the end of 2008--have arguably been the most important unconventional policy measures employed by the Federal Open Market Committee (FOMC) in recent years; see D' Amico, English, Lopez Salido, and Nelson  for a thorough discussion of the role of asset purchases in the broader context of monetary policy strategy.
Formally referred to as the Large-Scale Asset Purchase (LSAP) programs, or "quantitative easing" in popular parlance, the programs were designed to lower longer-term market interest rates by purchasing debt obligations of the government-sponsored housing agencies (GSEs), mortgage-backed securities (MBS) issued by those agencies, and coupon securities issued by the United States Treasury. In addition to conducting two LSAP programs during the 2008-10 period, the FOMC in the autumn of 2011 also initiated a Maturity Extension Program (MEP), in an effort to put further downward pressure on longer-term interest rates and thereby provide additional stimulus to economic growth.1
The rationale underlying LSAPs hinges on a presumption that the relative prices of financial assets are to an important extent influenced by the quantity of assets available to investors. Implicit in this view is a departure from the expectation hypothesis of the term structure of interest rates and an appeal to theories of "imperfect asset substitution," "portfolio choice," or "preferred habitat," theories that recently have received renewed attention and rigorous micro foundations in the work of Andres, Lopez Salido, and Nelson  and Vaanos and Vila . Indeed, in their communication of the likely effects of LSAPs on longer-term interest rates, policymakers have repeatedly invoked the preferred-habitat models of interest rate determination, as the canonical arbitrage-free term structure framework leaves essentially no scope for the relative supply of deeply liquid financial assets--such as nominal Treasuries--to influence their prices Kohn  and Yellen  .2
Given the unprecedented nature of the Federal Reserve's unconventional policy measures, a rapidly growing literature has emerged that tries to evaluate empirically the effects of the various asset purchase programs on financial asset prices. Perhaps not too surprisingly, the initial phase of this research has focused on the question of whether purchases of large quantities of Treasury coupon securities have altered the level of longer-term Treasury yields. Employing a high-frequency, event-style methodology Gagnon, Raskin, Remeache, and Sack , Swanson , Krishnamurthy and Vissing-Jorgensen , and Wright  present compelling evidence that the Federal Reserve's LSAP announcements had economically and statistically significant effects on Treasury yields. Consistent with this evidence, Greenwood and Vayanos [2010a], Gagno, Raskin, Raskin, Remache, and Sack , Krishnamurthy and Vissing-Jorgensen , and Hamilton and Wu  also show that Treasury supply factors have important effects on Treasury yields and the associated term premiums at lower frequencies and over longer sample periods.3
By cleverly exploiting the variation in prices across individual securities (CUSIPs) induced by the Federal Reserve's purchases of Treasury coupon securities, D'Amico and King , find strong evidence of localized supply effects in the Treasury market--that is, purchases of specific CUSIPs in the secondary market had economically and statistically significant effect on yields of both purchased securities and those at nearby maturities.4 Using a similar micro-level approach, D'Amico, English, Lopez Salido, and Nelson  attempt to disentangle the transmission channels involved in LSAPs and find that a significant portion of the variation in local supply and aggregate duration of Treasury securities was transmitted to longer-term Treasury yields through the term-premium component.5
Taking a different tack, Li and Wei  develop and estimate an arbitrage-free term structure model of interest rates that, in addition to observable yield curve factors, incorporates variables involving the relative supply of Treasury and agency mortgage-backed securities. The inclusion of the Treasury-supply factor is motivated by the work of Vayanos and Vila , while the inclusion of the MBS-supply factor reflects the market participants' perception of agency MBS as "safe" assets--and therefore close substitutes for Treasuries--owing to their earlier implicit and later explicit government guarantee. Both of these supply factors affect the term structure of interest rates primarily through the term-premium component, and according to Li and Wei  estimates, the combined effect of the three LSAPs resulted in a significant reduction in longer-term Treasury yields.
While economists have devoted the lion's share of attention to evaluating the effects of LSAPs on Treasury yields, considerably less attention has been paid to the question of whether LSAPs had an effect on yields of riskier assets.6 As emphasized by Krishnamurthy and Vissing-Jorgensen , LSAPs can affect private yields through different channels. In this paper, we focus on one particular channel--the "default risk" channel. Specifically, we quantify the effect that the announcements of the three asset purchase programs--through their impact on the risk-free rates--had on market-based measures of corporate credit risk, both in its broad economy-wide sense and on credit risk specific to the financial sector.7
The focus on the former is motivated by the fact that if LSAPs were successful in stimulating the economy by lowering the general level of interest rates, we should observe a reduction in expected defaults and, as a result, a decline in corporate borrowing costs. Moreover, as the economic recovery gains traction, some standard asset pricing models imply an associated reduction not only in the compensation demanded by investors for expected default risk, but also in the average price of bearing exposure to corporate credit risk, above and beyond the compensation for expected defaults--that is, a reduction in the default risk premium. This increase in investor risk appetite--by lowering the price of default risk--should put additional downward pressure on corporate borrowing rates and thereby further stimulate business fixed investment, an especially cyclically-sensitive component of aggregate demand.
The focus on credit risk specific to the financial sector, on the other hand, is motivated by an influential recent theoretical literature that stresses the implications of the capital position of financial intermediaries for asset prices and macroeconomic stability; see, for example, Brunnermeier and Sannikov  and He and Krishnamurthy , . The common thread running through these theories is that a deterioration in macroeconomic conditions, by depressing the capital base of financial intermediaries, induces a reduction in the risk-bearing capacity of the financial sector. To the extent that financial intermediaries are the marginal investors in asset markets, this effective increase in risk aversion causes a jump in the conditional volatility and correlation of asset prices and a sharp widening of credit spreads, a worsening of financial conditions that amplifies the effect of the initial shock on the macroeconomy.8
Any empirical investigation of the effect of LSAPs on corporate credit risk confronts a serious econometric challenge. First, yields of assets targeted by the central bank purchases--typically safe assets--may be simultaneously influenced by the movements in prices of risky financial assets, resulting in a difficult endogeneity problem. Second, the identification of the responsiveness of credit risk indicators to such policy interventions is complicated by the fact that a number of other factors, including news about the economic outlook and "flight-to-quality" consideration, likely had a significant effect on both the benchmark interest rates and market-based indicators of corporate credit risk during the period in which LSAPs were implemented.
In such circumstances, as we show below, the standard high-frequency, event-style regression analysis--which effectively assumes that the LSAP announcements are the sole source of volatility in the benchmark market interest rates on those days--will yield a downward-biased estimates of the coefficients measuring the effect of LSAPs on corporate credit risk indicators. Consistent with that observations, our event-style regression results indeed imply that the LSAP announcements had no impact on the cost of insuring against a broad-based incidence of defaults, as measured by the response of tradable credit derivative (CDX) indexes that are used widely by investors for hedging of and investing in corporate credit risk.9
To address these identification issues, we employ an alternative econometric approach developed by Rigobon  and Rigobon and Sack , , which allows us to identify the parameter of interest--the structural response coefficient measuring the reaction of CDX indexes to declines in the benchmark interest rates induced by the LSAP announcements--under a weaker set of assumptions than those employed in our event-style analysis. In this so-called identification-through-heteroskedasticity approach, the response of credit risk indicators to policy interventions is identified vis-à-vis the shift in the variance of monetary policy shocks associated with policy announcements. As in Wright , our identification strategy involves a natural assumption that the volatility of monetary policy shocks increased on the days of the LSAP announcements, precisely because a larger portion of the news hitting financial markets was about monetary policy.10
In contrast to our event-style regression results, the heteroskedasticity-based approach implies that the declines in benchmark market interest rates induced by the LSAP announcements led to economically large and statistically significant reductions in the CDX indexes, both for the investment and speculative-grade segments of the U.S.sector. The stark difference in the results from the two econometric approaches underscores the difficult identification issue of the so-called default risk channel of monetary policy transmission during the recent financial crisis, a period in which both policy rates--in this case yields on Treasuries, MBS, and agency debt--and credit risk indicators were likely reacting simultaneously to common shocks during days surrounding policy announcements. At the same time, our heteroskedasticity-based identification strategy implies that while the unconventional policy measures employed by the Federal Reserve to stimulate the economy in recent years have substantially lowered the overall level of credit risk in the economy, the LSAP announcements--somewhat to our surprise--had no measurable effect on credit risk in the financial intermediary sector.
The remainder of the paper is organized as follows. In Section 2, we discuss the LSAP announcement dates used in the analysis and present the necessary background evidence regarding the effect of those announcements on the key benchmark interest rates. Section 3 describes the construction of our credit risk indicators, both for the broad U.S.sector and those pertaining to the financial sector. Section 4 contains our main results. It begins with an event-style analysis of the impact of the LSAP announcements on corporate credit risk and then shows why such an analysis may lead to a downward bias in the OLS estimator of the coefficient measuring the response of credit risk indicators to the changes in the benchmark market interest rates prompted by the LSAP announcements. To address this issue, it proposes a heteroskedasticity-based estimator of this effect and presents our key findings; to examine the robustness of our result, subsection 4.3 zeroes in on the five largest U.S. institutions, which play a key role in both the traditional bank-like credit intermediation process and in the arm's length finance that takes place in securities markets. Section 5 offers a brief conclusion.
In this section, we present evidence from an event study of major announcements associated with the Federal Reserve's three asset purchase programs (LSAP-I, LSAP-II, and MEP). Gauging the effect of LSAP announcements on yields of assets purchased by the Federal Reserve--that is, Treasury coupon securities, agency MBS, and agency debt--provides the necessary backdrop against which to evaluate the effects of the three asset purchase programs on corporate credit risk.
To maintain comparability with previous studies, we focus on the event dates identified by Krishnamurthy and Vissing-Jorgensen . Within the standard taxonomy of the Federal Reserve's asset purchase programs, these event dates are as follows:
To obtain an estimate of the effect of various LSAP announcements on benchmark interest rates, we run the following event-style regression:
Table 1 summarizes the average effects of the program-specific LSAP announcements on yields of assets targeted by the three purchase programs.13According to the entries in the table, the effect of the first purchase program (LSAP-I) on market interest rates was substantial in economic terms: The average decline in longer-term Treasury yields induced by the five announcements was about 20 basis points, while yields on agency MBS and longer-term agency debt fell almost 25 basis
|Interest Rate||LSAP-I||LSAP-II||MEP||Pr > W a||R 2|
|Treasury (1y): Standard Errors||(0.024)||(0.002)||(0.002)|
|Treasury (5y):Standard Errors||(0.080)||(0.013)||(0.002)|
|Treasury (10y):Standard Errors||(0.087)||(0.033)||(0.002)|
|Agency MBSb: Standard Errors||(0.098)||(0.040)||(0.003)|
|S-T Agencyc :Standard Errors||(0.057)||(0.015)||(0.002)|
|M-T Agencyd:Standard Errors||(0.083)||(0.016)||(0.002)|
|L-T Agencye:Standard Errors||(0.109)||(0.029)||(0.002)|
The effects of the second purchase program (LSAP-II) on interest rates were also economically important, though not as widespread as those of the LSAP-I. According to our estimates, the average decline in the 5-year Treasury yield in response to the three LSAP-II announcements was about 7 basis points, while yields on medium-term (3-5 years) agency debt fell 4 basis points.
The last program undertaken by the Federal Reserve during our sample period (MEP) also had predictable effects, as the declines in interest rates were concentrated at the longer-end of the maturity spectrum. Indeed, as envisioned by the FOMC, the MEP significantly flattened the Treasury yield curve, both by depressing the long-end and by inducing a small rise at the short- and intermediate-end of the yield curve. All told, these results are consistent with the recent work of Gagnon, Raskin, Remache, and Sack , Krishnamurthy and Vissing-Jorgensen , D Amico, English, Lopez Salido, and Nelson , Wright , and D Amico and King , who document that asset purchase programs had significantly altered the level of longer-term government bond yields. They are also consistent with the event-style evidence presented by Swanson , who shows that the Federal Reserve's large purchases of longer-term Treasury securities during the 1961 Operation Twist had a major effect on financial markets.
This section describes the construction of credit risk indicators used in our analysis. In all instances, the indicators are based on financial derivatives on credit risk--that is, (single-name) credit default swaps (CDS)--instruments used extensively by investors for hedging of and investing in credit risk. A CDS is simply an insurance contract between two parties: a protection buyer, who makes fixed, periodic payments; and a protection seller, who collects these premiums in exchange for making the protection buyer whole in case of default.14 Although akin to insurance, CDS are not regulated by insurance regulators--they are over-the-counter (OTC) transactions--and unlike standard insurance contracts, it is not necessary to own the underlying debt in order to buy protection using CDS. In general, CDS trades take place between institutional investors and dealers, with the legal framework for trading governed by the International Swaps and Derivatives Association (ISDA).
Using CDS contracts to measure credit risk has a number of advantages over other commonly-used indicators of credit risk such corporate bond credit spreads. First, it is far easier to buy credit protection than to short corporate bonds. As a result, CDS are a natural vehicle for shorting default risk, which allows investors to take a specific view on the outlook for credit quality of a specific company. Second, the use of CDS contracts allows users to avoid triggering tax or accounting implications that arise from sales of actual bonds. Third, CDS contracts offer easy access to hard to find credits, reflecting a limited supply of bonds in many instances. And lastly, investors can more easily tailor their credit exposure to maturity requirements and desired seniority in the firm's capital structure. It should be noted that although CDS are available at various maturities, the 5-year contract is by far the most commonly traded and liquid segment of the market.
We rely on credit derivative indexes owned and managed by Markit as a comprehensive measure of credit risk at the broad, economy-wide level. Compared with other commonly-referenced financial indexes, such as indexes of corporate bond yields and spreads or equity indexes, credit derivative indexes are tradable products. Buying and selling of the credit derivative index is comparable to buying and selling portfolios of corporate cash instruments: By buying the index the investor takes on the credit exposure--is exposed to defaults--a position similar to that of buying a portfolio of bonds; by selling the index, the credit exposure is passed on to another party. As a result, investors can take positions directly in the indexes without having to trade a large number of underlying components--in fact, index trading is often thought to lead single-name CDS trading.
To capture the full spectrum of credit quality in the U.S. market, we consider two indexes: the (North American) 5-year CDX investment-grade index (CDX-IG); and the (North American) 5-year CDX speculative-grade index (CDX-SG). The investment-grade CDX index references 125 CDS on firms that have an investment-grade rating from both Moody's and Standard & Poor's at the time the index starts trading, while the speculative-grade CDX index references 100 CDS on firms that have a "junk" rating from at least one rating agency. Importantly, the component firms must have highly liquid single-name CDS trading in their name, and the composition of both indexes--which is determined by a dealer poll--reflects the composition of the U.S.sector.
All firms in the indexes are equally weighted, and the composition of both indexes is fixed once the indexes start trading.15 However, new vintages of the indexes are introduced every six months, and the new vintages may have different components than the old vintages. When a new vintage is introduced, it becomes the "on-the-run" vintage; previous versions of the indexes continue trading as "off-the-run" vintages.16 To ensure the maximum liquidity for our broad indicators of corporate credit risk, we spliced together the on-the-run vintages for both the investment- and speculative-grade CDX indexes.17
Figure 1 shows our two main credit risk indicators over the 2007-11 period. According to these two measures, credit risk in the U.S.sector increased noticeably with the onset of the financial crisis in the summer of 2007, likely reflecting the rapidly deteriorating outlook for the housing sector and associated concerns about the possible spill-over effects on financial institutions and the broader economy. Both indicators were exceptionally volatile during the subsequent recession and spiked sharply at critical events of the crisis: the collapse of Bear Stearns investment bank in March 2008; the bankruptcy of Lehman Brothers in mid-September 2008; and in early 2009, when continued pressures on already-strained balance sheets of financial intermediaries threatened
Note: The solid line depicts the 5-year (on-the-run) investment -grade CDX index (CDX-IG), while the dotted line depicts the 5-year (on-the-run) speculative-grade CDX index (CDX-SG). The shaded vertical bar represents the 2007-09 NBER-dated recession. LSAP-I announcement days are Nov-25-2008, Dec-01-2008, Dec-16-2008, Jan-28-2009, and Mar-18-2009; LSAP-II announcement days are Aug-10-2010, Sep-21-2010, and Nov-03-2010; and the MEP announcement date is Sep-21-2011.
Indeed, as shown by the thin vertical lines, the first round of asset purchases (LSAP-I) was launched in response to these adverse economic developments and to help stimulate economic growth. The flare-up in CDX spreads in the spring of 2010, which was part of a deterioration in broad financial conditions, largely reflected investors' concerns about European sovereign debt and banking issues as well as concerns about the durability of the global recovery. Although financial market conditions improved somewhat early in the second half of 2010--partly as investors increasingly priced in further monetary policy accommodation--the Federal Reserve initiated LSAP-II in the second half of the year, in response to indications of a slowing pace of economic recovery and persistent disinflationary pressures.
Financial markets were buffeted again over the second half of 2011 by changes in investors' assessments of the ongoing European crisis as well as in their evaluation of the U.S.outlook. As a result, the credit outlook for the corporate sector deteriorated markedly. With economic activity expanding only at a slow pace and with labor market conditions remaining weak, the FOMC launched the MEP with the intent to put further downward pressure on longer-term interest rates and to help make broader financial conditions more accommodative.
We now turn to the construction of credit risk indicators specific to the financial sector. In light of the above discussion, the most natural such indicator would be a credit derivative index, with its components referencing CDS contracts of a broad array of U.S.institutions. Such an index, unfortunately, does not exist. As an alternative, we use the single-name (North American) 5-year CDS contracts to construct simple indexes of credit risk for two types of financial institutions: commercial banks (CDS-BK) and securities broker-dealers (CDS-BD).
Our focus on these two types of financial intermediaries is motivated by the fact that a significant portion of the credit extended to businesses and households--both on- and off-balance-sheet--is through the commercial banking sector (Bassett, Gilchrist, Weinbach, and Zakraj'sek . Thus, our commercial bank CDS index captures the market-based assessment of the credit risk for the traditional class of financial intermediaries. In contrast, securities broker-dealers, a class of highly leveraged financial intermediaries, buy and sell a large array of securities for a fee, hold an inventory of securities for resale, and differ from other types of institutional investors by their active pro-cyclical management of leverage. As documented by Adrian and Shin , expansions in broker-dealer assets are associated with increases in leverage as broker-dealers take advantage of greater balance sheet capacity; conversely, contractions in their assets are associated with de-leveraging of their balance sheets. Reflecting their role as a "marginal investor," broker-dealers play an important role in most financial markets, and, as shown by Gilchrist and Zakrajsek , changes in their creditworthiness--as measured by the movements in their CDS spreads--are closely related to fluctuations in the effective risk-bearing of the broader financial sector.
To construct these credit risk indicators, we selected from the Markit's single-name database the daily 5-year CDS quotes for a sample of 26 U.S.banks and nine U.S.-dealers. In terms of the triggering events, we focus on the contracts with the Modified Restructuring (MR) clause, which, in addition to an outright default, considers any change in the nature of a company's financial liabilities in the absence of default as a credit event.18 Using these micro-level data, we
calculate CDS indexes for the portfolios of both commercial banks and broker-dealers as simple (unweighted) cross-sectional averages of the available spreads at each day.
As shown in Figure 2, the behavior of these two credit risk indicators over the recent crisis closely mimics that of the broader investment-grade U.S.sector--financial firms are rated almost exclusively as investment grade by the major rating agencies. One problem with the construction of these indexes is that the underlying micro-level panels are of unbalanced nature, as smaller and less prominent institutions have occasional gaps in their CDS series. The lack of reliable CDS quotes for certain institutions on certain days is most likely due to the sporadic impairment in the functioning of the credit derivatives market, especially during the most acute phases of the financial crisis. While the cross-sectional average of the component quotes provides an unbiased estimate of the average level of CDS spreads at any given point in time, our formal analysis relies on changes in the credit risk indicators. Because of potential changes in the composition of the indexes between two periods, taking first difference of the indexes shown in Figure 2 would, consequently, introduce a significant amount of noise in the differenced series, thereby complicating the interpretation of the results.
To deal with this problem, we first compute the difference of the daily CDS spreads for each component of the two indexes and then compute the cross-sectional (unweighted) averages for the two portfolios of financial firms. When calculating the cross-sectional averages of the 1-day changes in CDS spreads, we use trimmed means, which delete the smallest and largest change in CDS spreads from the sample at each point in time. By using such a robust estimator of the population mean, we mitigate the effects of extreme values that might arise from abrupt changes in the liquidity of the single-name CDS market.19
To examine the effect of the LSAP announcements on corporate credit risk, we first re-estimate the event-style regression specification (1), using changes in our four credit risk indicators as dependent variables. The results of this exercise are reported in Table 2. According to these results, the announcement effects of the first two asset purchase programs (LSAP-I and LSAP-II) had no
|Treasury (5y):Standard Errors||(0.042)||(0.136)||(0.034)||(0.065)|
|Agency MBSa: Standard Errors||(0.036)||(0.148)||(0.023)||(0.043)|
|Agency Debtb: Standard Errors||(0.041)||(0.170)||(0.030)||(0.059)|
In contrast, the MEP announcement is associated with a statistically significant increase in all four credit risk indicators, with the effect being most pronounced for the speculative-grade CDX index (33 basis points) and for the CDS spreads of broker-dealers (17 basis points). These sizable increases in the cost of insuring against corporate defaults induced by the MEP announcement are likely due to the fact that the announced size of the program was somewhat less than than markets had originally anticipated. At first glance, the results in Tables 1 and 2 suggest that while the LSAP announcements led to significant declines in the benchmark market interest rates, they had no obvious effect on the cost of insuring against a specter of corporate defaults, both in an economy-wide sense or against defaults specific to the financial sector.
To understand the lack of response of CDS spreads to the LSAP announcements, it is instructive to examine the relationship between changes in interest rates and changes in CDS spreads during the entire 2008-11 period. In Table 3, we report the coefficients from the regression of the daily change in each of our four credit risk indicators on the daily change in the three benchmark interest rates: the 5-year Treasury yield, the 30-year MBS rate, and the rate on longer-term agency bonds.
Several points about these results are worth noting. First, all the coefficients on interest rate changes are negative and highly significant, both in economic and statistical terms. This strong negative relationship implies that when longer-term risk-free rates were falling during the crisis, the cost of insurance against corporate defaults was rising sharply. Second, in terms of the type of interest rate, changes in Treasury yields appear to have had the largest economic impact on the CDS spreads, followed by changes in yields on longer-term agency bonds. And lastly, the largest (absolute) coefficients are associated with the speculative-grade firms (CDX-SG) and with the broker-dealers (CDS-BD), two relatively highly leveraged segments of the U.S.sector.
Two natural and related interpretations of these results spring to mind. The first is that the negative relationship between changes in the benchmark market interest rates and corporate credit risk is driven by adverse news to economic fundamentals, which signals a deterioration in the outlook for credit quality, reflecting a downward revision to future growth prospects. As a result, the cost of default insurance increases, while longer-term risk-free rates decline. This interpretation is consistent with the result that the negative relationship between changes in risk-free interest rates and CDS spreads is most pronounced for lower-rated corporate credits, a segment of the market that was especially vulnerable to adverse macroeconomic shocks during this period.
The second interpretation is that there are shocks to risk premiums that trigger a "flight-to-quality," a phenomenon that causes investors to dump risky assets to purchase safer investments. In that case, the expected returns on risky assets increase, while those on riskless assets fall. This interpretation is consistent with the result that, in absolute terms, the largest coefficients on interest rate changes are associated with Treasuries and longer-term agency bonds.20 As we show in the next section, both of these mechanisms will lead to a downward bias in the OLS estimates of the coefficient measuring the response of CDS spreads to changes in market interest rates induced by the LSAP announcements.
As emphasized by Rigobon and Sack [2003, 2004], causal inference regarding the impact of policy announcements on asset prices may be difficult in an environment where both policy rates--in our case yields on Treasuries, MBS, and agency debt--and asset prices endogenously respond to common shocks in periods surrounding policy announcements. To illustrate the argument more formally, let denote the change in yields on safe assets that are directly influenced by the LSAP announcements, and let denote the change in yields on risky corporate assets, as measured by the changes in the relevant CDS spreads. Furthermore, let denote a common shock that simultaneously affects both CDS spreads and risk-free interest rates, and let represent policy shock--the LSAP announcement--while is a shock to CDS spreads that is independent of the common shock . It is assumed that disturbances , , and are homoskedastic with variances , , and , respectively.
The response of interest rates and CDS spreads to various shocks is captured by a simultaneous system of equations of the form:
The reduced-form of the system (2)-(3) is given by
Note that the bias of the OLS estimate of the coefficient is a decreasing function of , the variance of the policy shock. As becomes large relative to and , the bias disappears. As emphasized by Rigobon and Sack , high-frequency event studies that use OLS to estimate the effect of changes in policy rates on financial asset prices effectively assume that is the only source of volatility on policy announcement days. Given the heightened volatility and strains that characterized financial markets during this period, this seems a questionable assumption.
Building on the work of Rigobon , Rigobon and Sack  propose an estimator for the response coefficient that is identified through the fact that the volatility of policy shocks increases on policy announcement days.22 The essential idea is that by knowing a priori on which dates the variance of policy shocks shifts, the researcher is able to identify the response of asset prices to changes in policy rates under a fairly weak set of assumptions by looking at changes in the comovement between policy rates and financial asset prices.
More concretely, let denote a subset of policy announcement days and denote a subset of non-announcement days. Furthermore, let
As shown by Rigobon and Sack , these two estimators can be obtained from a simple instrumental variables (IV) procedure. To see this, define and as the vectors of stacked data corresponding to the two subsamples (i.e., ); let denote the vector such that if and zero otherwise; and let , where denotes the vector of ones. Define
In this so-called identification-through-heteroskedasticity approach, the response coefficients and can be estimated separately by 2SLS, but one can also impose a restriction that and estimate using GMM:
To operationalize this approach, we must specify the and subsamples. The subsample naturally contains the nine LSAP announcement dates used in the event-style analysis. The subsample comprises the remainder of the Jan-02-2008-Dec-30-2011 sample period with the following exceptions: First, we eliminated all days associated with non-LSAP policy announcements that could have left an imprint in financial markets. These non-LSAP announcements include communication associated with the FOMC meetings, release of the FOMC minutes, and major speeches and Congressional testimonies by the FOMC participants. The exclusion of these days, most of which contain some indirect news about unconventional policy measures, serves to sharpen the distinction between the two covariance matrices that is crucial for identification.
And lastly, given that our sample period is characterized by an exceptional turmoil in financial markets, we also eliminated from the subsample a small number of dates associated with extreme changes in the two CDX indexes, a move designed to mitigate the effect of outliers on our estimates. Specifically, we dropped from the subsample all days where the change in either investment- or speculative-grade CDX index was below the 1st or above the 99th percentile of its respective distribution. All told, this procedure yielded 673 observations for the final subsample .23
In presenting our main results, we consider three estimators of , the coefficient measuring the response of credit risk indicators to changes in the risk-free rates induced by the LSAP announcements: (1) HET-1: a 2SLS estimator of the coefficient obtained from an IV regression of on , using as an instrument (this corresponds to the estimator above); (2) HET-2: a single-equation GMM estimator of that uses both and as instruments (this corresponds to the estimator above); and (3) HET-3: a system GMM estimator of the vector of coefficient that uses all valid instruments when analyzing the response of multiple credit risk indicators to the changes in the benchmark market interest rates. In both instances involving GMM estimation, the optimal weighting matrix is obtained from a two-stage procedure that sets the weighting matrix equal to the identity matrix in the first stage and then computes the optimal weighting matrix based on the first-stage estimates.
The top panel of Table 4 reports the results for the case where corresponds to change in the 5-year Treasury yield. Using the 2SLS estimator (HET-1), the estimates of for both investment- and speculative-grade CDX indexes are positive but imprecisely estimated and hence are not statistically different from zero at conventional significance level. Although the standard errors are of the same order of magnitude across the three estimators, the point estimates of the response coefficient increase notably as we move from a single-equation 2SLS estimation to either the single-equation or system GMM estimation methods. In particular, the system GMM estimator (HET-3) yields an estimate of the response coefficient for the investment-grade CDX index (CDX-IG) of 0.382 and an estimate of 1.285 for its speculative-grade counterpart (CDX-SG). In addition to being statistically significant, one cannot reject--using the Hansen  -test--the over-identifying restrictions for either the single-equation or system GMM estimators.
As shown in the bottom two panels, using the agency MBS rate or the agency bond yield in place of the 5-year Treasury yield to measure changes in risk-free interest rates produces very similar results. In all instances, the estimates of the coefficient are positive, economically meaningful, and they become statistically highly significant with the use of more-efficient GMM methods. Again, as indicated by the -values of the -test, we do not reject the over-identifying restrictions in all the cases.
The results in Table 4 imply that the cost of insuring against broad-based incidence of defaults in the U.S.sector declined significantly in response to a drop in yields on safe assets induced
|Interest Rate: Treasury (5y)||Interest Rate: Treasury (5y):Credit Risk Indicator||Interest Rate: Treasury (5y) HET-1||Interest Rate: Treasury (5y):HET-2||Interest Rate: Treasury (5y): HET-3|
|Interest Rate: Agency MBSc Credit Risk Indicator||Interest Rate: Agency MBSc HET-1||Interest Rate: Agency MBSc HET-2||Interest Rate: Agency MBSc HET-3|
|CDX-IG: Standard Errors||(0.140)||(0.097)||(0.084)|
|CDX-SG: Standard Errors||(0.618)||(0.182)||(0.340)|
|Interest Rate: L-T Agencyd Credit Risk Indicator||Interest Rate: L-T AgencydHET-1||Interest Rate: L-T AgencydHET-2||Interest Rate: L-T AgencydHET-3|
|Interest Rate: Treasury (5y)Credit Risk Indicator||Interest Rate: Treasury (5y)HET-1||HET-2||HET-3|
|CDX-BK: Standard Errors||(0.040)||(0.028)||(0.042)|
|CDX-BD: Standard Errors||(0.057)||(0.058)||(0.108)|
|Interest Rate: Agency MBScCredit Risk Indicator||Interest Rate: Agency MBScHET-1||Interest Rate: Agency MBScHET-2||Interest Rate: Agency MBScHET-3|
|Interest Rate: L-T AgencydCredit Risk Indicator||Interest Rate: L-T AgencydHET-1||Interest Rate: L-T AgencydHET-2||Interest Rate: L-T AgencydHET-3|
by the LSAP announcements. Moreover, the estimates of the response coefficients that rely on the more-efficient GMM methods indicate that the decline in the speculative-grade CDX index is three to four times as large as in the investment-grade segment of the corporate sector. In principle, this sizable reduction in the cost of default insurance should be reflected in a substantially lower business borrowing costs, especially for riskier credits. According to our estimates, a reduction of 25 basis points in longer-term market interest rates induced by an LSAP announcement lowers the 5-year CDS premium for a typical junk-rated firm by about the same amount, translating into a drop of 50 basis points or more in the level of interest rates faced by such a firm.
These results are consistent with those of Krishnamurthy and Vissing-Jorgensen , who find that the first asset purchase program (LSAP-I) significantly lowered CDS spreads on lower-rated corporate bonds. They are also consistent with the recent work of Wright , who shows that monetary policy shocks had a significant--though fairly short lived--effect on corporate bond yields during the period when short-term rates were stuck at the zero lower bound. In general, the results in Table 4 comport with our earlier discussion, which argued that an OLS event-type estimator of changes in CDS spreads on the LSAP announcement indicators is likely to be biased downward, relative to an estimator that controls for the simultaneity between changes in the benchmark market interest rates and CDS spreads during the crisis period.
We now turn to the impact of the LSAP announcements on the market perception of credit risk in the financial sector. Table 5 summarizes the results from IV regressions, in which changes in the CDS spreads for our two types of financial intermediaries are regressed on changes in the benchmark interest rates. The striking feature of these results is that, regardless of the estimation procedure or the choice of the benchmark interest rate, all estimates of the structural response coefficient are statistically indistinguishable from zero; moreover, the estimates are essentially zero in economic terms. Thus, in contrast to the response of broad, economy-wide indicators of corporate credit risk, the results in Table 5 indicate that the declines in risk-free rates induced by the LSAP announcements had no discernible effect on the CDS spreads of U.S.banks or broker-dealers. In fact, our results imply that in a relative sense, the market views the financial intermediary sector as riskier as a result of LSAPs.
To the extent that loans extended by financial intermediaries, along with their other financial investments, should be less likely to default or deliver subpar returns when the broad-based indicators of corporate credit risk fall, these results appear puzzling. One possible explanation for these findings is that the profitability of the financial sector--the primary purpose of which is to perform maturity transformation--declines when longer-term interest rates fall relative to short-term interest rates. This interpretation is consistent with the recent work of English, Van den Heuvel, and Zakrajsek , who document that the return on assets in the U.S.banking sector drops sharply in response to the flattening of the Treasury yield curve, reflecting the ensuing compression of banks' net interest margins and deposit disintermediation that shrinks banks' balance sheets.
At the time when the financial sector is already facing significant capital and liquidity pressures, an LSAP-induced reduction in longer-term interest rates would put a further downward pressure on the sector's profitability, which may cause the CDS spreads of financial institutions to remain unchanged because the deterioration in their near-term creditworthiness outweighs the improvement in the economic outlook. An alternative possibility is that the various LSAP announcements reinforced the investors' perception that the government may impose significant losses on the holders of unsecured debt claims issued by the financial sector because LSAPs eliminated the extreme tail risk associated with the systemic financial crises. If market participants believed that the wholesale government bailout of the financial sector--which would have been more likely in the case of an extreme deterioration in economic conditions and in which all creditors would also likely be made whole--was less probable as a consequence of LSAPs, the financial sector CDS spreads might not fall, even as the LSAP announcements induce a decline in the broad market-based indicators of corporate credit risks
One potential critique of the above analysis is that the single-name CDS spreads of banks and broker-dealers in the two sectoral indexes are not as liquid as the components of the tradable CDX index. As a result, our credit risk indicators for the financial sector may not respond to the LSAP announcements in a sufficiently timely manner.24 In addition, to the extent that credit risk in the U.S.sector during the crisis was concentrated at the largest institutions, the focus on the average change in bank or broker-dealer CDS spreads may not be very indicative of how LSAPs may have altered the market's perception of credit risk in the financial sector.
As a final exercise, therefore, we focus on the CDS spreads of the five largest and most prominent U.S.Holding Companies (FHCs): JPMorgan Chase & Co., Citigroup Inc., Bank of America Corp., Goldman Sachs Group Inc., and Morgan Stanley. Reflecting their systemic importance, the financial health of these FHCs was of direct concern to both policymakers and market participants during the crisis. As a result, the CDS contracts written on these companies are highly liquid.25Through their commercial bank subsidiaries, these FHCs also engage in the traditional provision of credit to businesses and households, while at the same time pursuing nonbanking activities that offer customers a wide range of financial services, including the opportunity to invest in securities and, in some instances, to purchase insurance products; they also operate highly leveraged broker-dealer subsidiaries, which as argued above, play an important role in financial markets.
Figure 3 shows the CDS spreads for these systemically important global financial institutions. The investors' perception of credit risk associated with these five institutions clearly changed significantly with the onset of the financial crisis in the summer of 2007. Among them, the two former investment banks, Goldman Sachs and Morgan Stanley, had the most volatile CDS spreads, reflecting, in part, their business models that involved the use of high leverage and heavy reliance on short-term funding to engage in maturity transformation. Not surprisingly, there is a high degree of comovement in the CDS spreads of these five institutions; indeed, reflecting their common exposure to the macroeconomic risk factors, the first principal component explains about 75 percent of the variability in CDS spread changes over the 2008-11 sample period.
|Treasury||Agency MBS||L-T Agency|
|Financial Holding Company: JPMorgan Chase||-0.131*||-0.054||-0.057|
|Financial Holding Company: JPMorgan Chase: Standard Errors||(0.072)||(0.041)||(0.064)|
|Financial Holding Company:Citigroup||-0.184***||-0.201**||-0.150***|
|Financial Holding Company:Citigroup: Standard Errors||(0.035)||0.043)||(0.044)|
|Financial Holding Company: Bank of America||-0.102*||-0.198***||-0.106***|
|Financial Holding Company: Bank of America: Standard Errors||(0.062)||(0.072)||(0.035)|
|Financial Holding Company: Goldman Sachs||-0.140||-0.121*||-0.089|
|Financial Holding Company: Goldman Sachs:Standard Errors||(0.096)||(0.062)||(0.060)|
|Financial Holding Company: Morgan Stanley||0.008||0.051||0.054|
|Financial Holding Company: Morgan Stanley:Standard Errors||(0.130)||(0.105)||(0.128)|
To examine formally the impact of the LSAP announcements of the CDS spreads of these institutions, we employ a system-GMM estimator, which allows us to estimate simultaneously the response of the institutions-specific CDS spreads to the LSAP-induced changes in market interest rates. In the estimation, we allow the response coefficient to differ across the five FHCs. According to the entries in Table 6, the estimated response of the CDS spreads to a decline in the benchmark market interest rates prompted by the LSAP announcements is negative, economically meaningful and consistently statistically significant for only Citigroup and Bank of America; for the remaining three institutions in our sample, the estimates of the response coefficients are generally much smaller in economic terms and in most cases statistically indistinguishable from zero.
These results are broadly consistent with those in Table 5, which showed that the average CDS spreads in the financial intermediary sector did not react to the LSAP-induced reductions in market interest rates. The fact that the cost of insuring against the default of Citigroup and Bank of America rose in response to the LSAP announcements is likely due to the widespread market perception that these two institutions were particularly battered by the recent financial crisis, a perception buttressed by their inability to pass the Federal Reserve's stress tests during that period. To the extent that the financial sector's return to high and sustained profitability was likely to take a considerable amount of time in an environment characterized by a flat term structure and weak economic growth, it is plausible that the LSAP-induced declines in longer-term interest rates caused market participants to reassess the credit risk of these two specific institutions, especially given their relatively weak capital positions.
In this paper, we analyzed the impact of changes in the benchmark market interest rates prompted by the LSAP announcements on the market-based indicators of corporate credit risk. Importantly, we used the identification-through-heterogeneity approach advocated by Rigobon  and Rigobon and Sack [2003, 2004] to correct for the simultaneity bias that plagues the standard event-style analysis. This approach, which allows us to identify more cleanly the structural response of CDS spreads to the LSAP-induced declines in market interest rates, indicates that the policy announcements led to a significant reduction in the cost of insuring against defaults for both investment- and speculative-grade corporate credits. In conjunction with the results of Hancock and Passmore , who find that the Federal Reserve's purchases of agency MBS led to a significant reduction in residential mortgage rates, our results thus support the view that LSAPs induced a significant easing of financial conditions in both the household and business sectors.
While the unconventional policy measures employed by the Federal Reserve to stimulate the economy appeared to have lowered the overall level of credit risk in the economy, they had no measurable impact on credit risk specific to the financial sector. This apparently puzzling result could reflect the fact that the flattening of the yield curve engineered by LSAPs reduced the future profitability of financial institutions that intermediate funds across maturities, which outweighed the improvement in the economic outlook, leaving the CDS spreads of financial firms unchanged on balance.
Alternatively, the CDS spreads of financial institutions may have failed to decline (or even increased) because the announcements of the purchase programs led market participants to lower their perceived likelihood of wholesale bailouts of the financial sector, situations in which the bondholders would likely suffer only minimal losses. To the extent that LSAPs eliminated the extreme tail risk associated with the systemic financial crises, investors may have realized that the government is more likely to impose greater losses on the holders of unsecured debt claims issued by financial firms, a reassessment of risk that would have boosted the financial sector CDS spreads relative to broad market-based measures of corporate credit risk.