Linkages Between the Financial and Real Sectors: An ... - CiteSeerX

Linkages Between the Financial and Real Sectors: An Overview Simon Gilchrist∗

Egon Zakrajˇsek†

September 24, 2008

Prepared for the Academic Consultants Meeting, “Financial Stability and Linkages Between Financial and Real Sectors,” Federal Reserve Board, October 3, 2008. We thank Dan Sichel for helpful conversations. ∗ Department of Economics Boston University and NBER. E-mail: [email protected] † Division of Monetary Affairs, Federal Reserve Board. E-mail: [email protected]

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Introduction

The United States is currently in the throes of an acute liquidity and credit crunch, by all accounts, the severest financial crisis since the Great Depression. The roots of this crisis lie in the collapse of the subprime mortgage market in the wake of an unprecedented and unexpected fall in house prices. The financial turmoil subsequently spread to a variety of other asset markets, causing massive liquidity problems in interbank funding markets, the sudden collapse of several major financial institutions, and a sharp reduction in lending activity; see Brunnermeier [2008] for a detailed account of the 2007–08 financial crisis. In the hope of preventing the financial meltdown from engulfing the real economy, the government in recent days announced plans to purchase a broad range of mortgage-related assets from a variety financial institutions, an intervention in the global financial markets at an unprecedented scale. In this essay, we assess the likely implications of such financial disruptions for the real economy. We begin the analysis with a brief snapshot of the current state of affairs and compare current trends in economic activity with those experienced during the last two recessions. We then discuss various linkages between the financial sector and the real economy and identify three main channels by which disruptions in financial markets can influence real activity: a pullback in spending owing to reductions in wealth; balance sheet mechanisms that lead to a widening of credit spreads, which curtail the ability of households and businesses to obtain credit; and the direct effect of impairments in the ability of financial institutions to intermediate credit. Although these channels are well-understood from a theoretical perspective, assessing their quantitative implications remains a considerable challenge for macroeconomists. For example, a fall in output that follows a drop in lending associated with a major financial disruption reflects both supply and demand considerations. In addition, in a world with a rapidly changing financial landscape, it is difficult to gauge the extent to which various financial asset market indicators provide consistent and credible information about the relationship between the health of the financial system and economic activity. Our analysis consists of two parts. First, we review the recent empirical evidence on the relationship between corporate credit spreads—the difference in yields between various corporate debt instruments and Treasury securities of comparable maturity—and economic activity, a link elucidated by the theoretical literature that emphasizes movements in default-risk indicators as an important signal of disruptions in financial markets. We extend the standard analysis by attempting to separate empirically the portion of the predictive content of default-risk indicators for economic activity that reflects the usual countercyclical movements in expected defaults from the part due to cyclical changes in the relationship

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between measures of expected default risk and credit spreads. According to our results, most of the information content of corporate credit spreads can be attributed to deviations in the pricing of corporate securities relative to the expected default risk of their issuer, a finding that suggests that signals about impending financial disruptions embedded in prices of corporate debt instruments may account for a significant portion of the forecasting power of credit spreads for economic activity. To provide further insight into the linkages between the financial sector and the real economy, we then discuss recent work that seeks to incorporate financial market frictions into otherwise standard dynamic stochastic general equilibrium (DSGE) models. The aim of this vein of research is to disentangle movements in the supply and demand for credit by imposing a structural framework on macroeconomic data. In particular, using quarterly U.S. data over the 1985:Q1–2008:Q2 period, we estimate a DSGE model based on the financial accelerator framework developed by Bernanke, Gertler, and Gilchrist [1999].1 Although the estimated model is relatively simple compared with other work in this area, the results nonetheless provide a considerable insight into the importance of financial factors in business cycle fluctuations. In particular, the model estimates suggest that financial disruptions are responsible for sharp declines in output growth during the last two recessions and that the easing of financial conditions during the second half of the last decade contributed importantly to the investment boom of the late 1990s. In addition, the model estimates imply that the current financial crisis—through its impact on business fixed investment— appears to be responsible for a considerable portion of the observed slowdown in economic activity.

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Current Economic Conditions

In this section, we offer a brief assessment of recent economic developments. To do so, we compare the time-series evolution of key macroeconomic variables during the current episode to their evolution during the last two NBER-dated recessions. Figure 1 considers the behavior of real GDP and its major components; Figure 2 examines the evolution of monthly indicators of labor market conditions and economic activity; and Figure 3 focuses on the housing sector. All series in the three figures are plotted as indexes that equal 100 during the quarter (month) that marks the beginning of the recession as dated by the NBER. For the current episode, all quarterly (monthly) series are benchmarked so that they equal 100 in 2007:Q2 (October 2007). This past summer marked a one-year anniversary of the current financial crisis. Accord1 Other formulations of financial market frictions in general equilibrium models include, for example, Fuerst [1995], Carlstrom and Fuerst [1997], Kiyotaki and Moore [1997], and Cooley, Marimon, and Quadrini [2004].

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Figure 1: Real GDP and Its Selected Components Real GDP

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ing to Figure 1, however, there is very little evidence that this turmoil has had a significant negative effect on aggregate demand, as evidenced by the behavior of real GDP, consumption, and business fixed investment. In contrast, output and consumption spending fell markedly at the onset of the 1990–91 recession, and business fixed investment peaked sev3

eral quarters prior to the 1990:Q3 cyclical peak. Business expenditures on fixed capital also dropped sharply several quarters prior to the onset of the 2001 recession, though the deceleration in output and consumption was substantially less severe in 2001 relative to that experienced during the 1990–91 recession. The cyclical downturn in 2001 was driven in large part by the collapse of investment in high-tech equipment that followed the bursting of the “tech bubble.” The recession of the early 1990s, in contrast, was considerably more broader-based, and the slowdown in economic growth reflected, in part, the effects of “financial headwinds” that impinged on consumer spending. A major difference between the current episode and the previous two recessions can be seen in the behavior of residential investment, a component of aggregate demand that peaked in 2005:Q4 and has fallen 60 percent over the subsequent 10 quarters. Figure 2 examines the evolution of labor market conditions and high-frequency indicators of production and spending. Compared with the GDP and its selected components, the dynamics of labor market indicators (the unemployment rate and private payroll employment) in the current situation match fairly closely the changes in labor market conditions during the previous two recessions. Similarly, output in the factory sector—as measured by the index of manufacturing industrial production—is beginning to show declines that appear consistent with those of the past two cyclical downturns. By contrast, growth of retail sales has remained relatively robust throughout the current period, though consumer spending has shown some signs of weakness in recent months. Figure 3 takes a look at the housing sector. As shown in the top left panel, (nominal) house prices, as measured by the OFHEO purchase-only index, expanded briskly through 2005 before slowing noticeably in 2006. House prices were essentially flat through 2007, and the first half of 2008 saw an outright decline in the OFHEO house price index. Consistent with the deceleration in house prices, housing starts and sales of both new and existing homes have plummeted from their peak levels reached early in 2006—housing starts have dropped a whopping 100 percent, and home sales have fallen nearly 60 percent. This weakness in the housing sector is also reflected in the massive buildup of new-home inventories, as evidenced by the run-up in the months’ supply of new homes, which surged almost 60 percent over this period. In contrast, activity in the housing sector showed no signs of slowing during the 2001 recession, and the downturn of the early 1990s was characterized by a relatively modest slowdown in housing starts and a minor inventory buildup. In summary, the seeds of the current economic slowdown can be traced to the sharp slowdown in house price appreciation and the drop in residential investment that has occurred since early 2006. Somewhat remarkably, both housing starts and home sales have remained relatively stable—albeit at very weak levels—in recent months despite the tightening of conditions in mortgage credit markets. Similarly, the growth of real GDP, business

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Figure 2: Labor Market, Production, and Spending Indicators Unemployment rate

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fixed investment, and consumption has been relatively well maintained during this period of prolonged financial turmoil. In contrast, the sharp deterioration of labor market conditions, along with the recent contraction in industrial output, provide compelling evidence 5

Figure 3: Housing Sector OFHEO house price index

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Note: The panels of the figure depict the behavior of the OFHEO purchase-only house price index, housing starts; home sales (new and existing homes), months’ supply of new homes at and around cyclical peaks as dated by the NBER. The black and blue lines are indexed to equal 100 during the quarter (month) marking the beginning of the NBER-dated recession. The red lines—the current episode—are indexed to equal 100 in 2007:Q4 (October 2007).

that economic growth is stalling. Moreover, these indicators suggest that the current deceleration in economic activity is comparable—both in its timing and magnitude—to the slowdowns that occurred during the previous two recessions. 6

3

Finance and the Real Economy

The benchmark macroeconomic model used to study the behavior of firms and households is predicated on the assumption that the composition of agents’ balance sheets has no effect on their optimal decisions. Within this Modigliani-Miller paradigm, households make consumption decisions based solely on permanent income—the sum of their financial wealth and the per-period income obtained from the present discounted value of future wages. Movements in financial asset prices shape agents’ spending decisions to the extent that they influence households’ financial wealth, whereas changes in interest rates affect spending decisions because they alter the present discounted values and hence reflect appropriately calculated user-costs for financing real consumption expenditures. On the business side, firms make investment decisions by comparing the expected marginal profitability of new investment projects with the appropriately calculated after-tax user-cost of capital. The relevant interest rate used in such calculations reflects the maturity-adjusted risk-free rate of return appropriate to discount the future cash flows. Financial market imperfections—owing to asymmetric information or moral hazard on the part of borrowers vis-` a-vis lenders—provide a theoretical link between the agents’ financial health and the amount of borrowing and hence economic activity in which they are able to engage. Although models differ on details, contracts between borrowers and lenders generally require that borrowers post collateral or maintain some stake in the project in order to mitigate the contracting problems associated with such financial market imperfections. For example, when the borrower’s net worth is low relative to the amount borrowed, the borrower has a greater incentive to default on the loan. Lenders recognize these incentive problems and, consequently, demand a premium to provide the necessary external funds. In general, this external finance premium is increasing in the amount borrowed relative to the borrower’s net worth. Because net worth is determined by the value of assets in place, declines in asset values during economic downturns result in a deterioration of borrowers’ balance sheets and a rise in the premiums charged on the various forms of external finance. The increases in external finance premiums, in turn, lead to further cuts in spending and production. The resulting reduction in economic activity causes asset values to fall further and amplifies the economic downturn—the so-called financial accelerator mechanism. Although the theoretical impact of changes in financial conditions on household and business spending decisions through the financial accelerator mechanism is well understood, quantifying the overall strength of this mechanism remains a challenge for macroeconomists. This task is complicated by the fact that it is very difficult to distinguish the effect of a slowdown in economic activity on household and firm spending owing to the usual demand channels absent financial market frictions from the effect that such a slowdown may have

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through the financial accelerator itself. Nonetheless, a careful assessment of the empirical implications of models that allow for financial frictions relative to those that assume perfect capital markets have allowed researchers to make substantial progress in assessing the empirical relevance of changes in financial conditions for real activity. On the household side, the permanent income model of consumption has stark implications for the responsiveness of consumption to both income and asset values. Transitory changes in income have very little effect on permanent income and hence consumption. Reasonably calibrated versions of such models imply that households are relatively insensitive to changes in asset values, suggesting that households should increase consumption by three to four cents for every dollar increase in their financial wealth. More importantly, to a first approximation, the value of housing does not represent net wealth for the household sector, because an increase in house values is also an increase in the implicit rental cost of housing. As a result, the household sector is no better or worse off when housing values rise; see Buiter [2008] for a thorough discussion. Empirical research provides compelling evidence against the permanent income model of consumption in favor of models in which the quality of household balance sheets plays an important role in determining their consumption decisions. A variety of studies shows that household consumption is excessively sensitive to movements in transitory income. Whereas the exact cause of this excess sensitivity is subject to a considerable debate, the excess sensitivity is generally attributed to the fact that at least a subset of households faces significant borrowing constraints or engages in precautionary-savings behavior because of imperfect insurance. Similarly, in contrast to the predictions of the permanent income model, both microeconomic and macroeconomic studies suggest an important link between house prices and household consumption (see, for example, Case, Quigley, and Shiller [2005]; Carroll, Otsuka, and Slacalek [2006]; and Campbell and Cocco [2008]). Estimates of the housing wealth effect vary but generally imply that household consumption increases by an amount ranging from 3 to 10 cents for every dollar increase in housing wealth. This response is generally attributed to the fact that at higher equity levels, households can obtain larger home mortgage loans and thus maintain high consumption levels while financing a home. Similarly, existing home owners may engage in mortgage equity withdrawals to finance high levels of consumption relative to their income. Although the estimated sensitivity of consumption to housing values appears small, the significant decline in U.S. house prices experienced during the last two years would imply a substantial drag on household consumption. Empirical research also provides evidence that supports the notion that corporate balance sheets influence investment spending, though this evidence is more contentious. It is well known that business investment spending is strongly correlated with corporate cash flow. Earlier research, initiated by Fazzari, Hubbard, and Petersen [1988], has argued that

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cash flows stimulate investment because internal funds are a cheaper source of finance than external funds. Critics, however, point out that current cash flows may also provide signals about future profits, which, in turn, determine the firm’s net worth and hence the strength of its balance sheet. That said, the available evidence suggests that the cash flow mechanism is quite strong for smaller firms, firms with a limited access to corporate credit and equity markets, or firms with weak balance sheets (see, for example, Gilchrist and Himmelberg [1995]). Some of more recent research has questioned the macroeconomic relevance of this effect by arguing that for large firms that account for the bulk of investment spending, current cash flows serve mainly as signals about future profit opportunities rather than indicators of the strength of their balance sheets (see, for example, Cummins, Hassett, and Oliner [2006] and Rebelo, Eberly, and Vincent [2008]). Nonetheless, studies that analyze investment spending during financial crises show that large negative shocks to firms’ balance sheets can have important adverse consequences for the investment decisions of large firms, at least during periods of acute financial distress (see, for example, Aguiar [2005] and Gilchrist and Sim [2007]). According to the currently available data, corporate balance sheets appear to be in relatively good conditions. The amount of liquid assets on the balance sheets of nonfinancial firms is high by historical standards, and corporate profits have been surprisingly well maintained in light of the persistent strains in financial markets. At the same time, credit spreads on a wide variety of corporate debt instruments have widened significantly since the middle of last year, a development that is consistent with a deterioration in the overall financial condition of the corporate sector or a worsening of conditions within the financial sector that serves as an originator and guarantor of corporate debt instruments. Although macroeconomic evidence offers a mixed guidance on the importance of interest rates for investment spending, recent work by Gilchrist and Zakrajˇsek [2007] using firm-level data shows that investment is highly responsive to changes in corporate credit spreads. Thus, although the corporate sector has maintained relatively strong balance sheets, it is still the case that rising credit spreads may be reducing current investment spending. (We return to this issue below.) The financial mechanism linking balance sheet conditions of borrowers to real activity is often described as the “broad credit channel.” Financial institutions are also likely to suffer from asymmetric information and moral hazard problems when raising funds to finance their lending activities. The focus of this so-called “narrow credit channel” is the health of financial intermediaries and its impact on the ability of financial institution to extend credit. In a fractional reserve banking system, deposits provide a source of funds for lending with only a small fraction of total deposits held as reserves. Because a tightening of monetary policy drains reserves from the banking system, poorly capitalized banks that are unable

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to raise external funds cut back on their lending. Bank-dependent borrowers, in particular small firms and households that have few alternative sources of credit, reduce spending. Kashyap and Stein [2000] document the empirical validity of this mechanism by showing that small U.S. commercial banks that are poorly capitalized are especially sensitive to changes in the stance of monetary policy. Although this bank lending channel appears to have important effects on the lending behavior of smaller banks, such banks account for only a small fraction of total bank lending in the United States, which suggests that the bank lending channel may not be a quantitatively important channel through which monetary policy affects the real economy. In a recent paper, however, Cetorelli and Goldberg [2008] argue that this lending channel may also be at work at large commercial banks operating primarily in domestic markets. In contrast, commercial banks with global operations are able to offset declines in domestic deposits through internal funds obtained from their global subsidiaries. In times of a worldwide financial distress, however, the ability of global subsidiaries to provide internal funds to U.S. financial institutions is also likely to be limited in scope, a development that would further strengthen the bank lending channel. Although monetary policy may not have a large direct impact through the bank-lending channel, reductions in bank capital during economic downturns can also reduce lending activity. As economic activity slows and defaults rise, the quality of bank loan portfolios deteriorates. Banks seeking to shore up their capital or to meet regulatory capital requirements tighten their credit standards and cut back on lending, an inward shift in loan supply that curtails spending of bank-dependent borrowers (see, for example, Van den Heuvel [2007].) The strength of this mechanism depends on the overall health of the banking sector and on the extent to which firms and households are bank dependent. In the United States, the bulk of investment spending is financed by relatively large firms that rely primarily on corporate bond and equity markets to finance their capital expenditures. Nonetheless, certain corporate debt instruments—most notably commercial paper—are typically backed by lines of credit at commercial banks. In addition, a substantial portion of business financing through commercial and industrial loans relies on such credit lines. In times of financial turmoil, even large nonfinancial firms may have a difficult time raising capital in arms-length markets. As these firms tap their backup lines of credit to finance inventories or operating expenditures in the face of falling revenues, banks may be forced to make further cuts in lending to bank-dependent borrowers. In summary, the recent drop in house prices is likely to bring about a reduction in consumption spending through its impact on household borrowing and mortgage equity withdrawals. In addition, the usual wealth channel implies that recent declines in stock prices may also reduce household consumption, though empirical estimates suggest this effect is likely to be relatively modest. Because nonfinancial corporate balance sheets remain

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relatively strong, investment spending is likely to remain relatively solid but will slow to the extent that corporate borrowing rates remain elevated because of persistent and intensifying strains in financial markets. The direct effect of falling values of assets held by the financial sector is more difficult to assess. Although there is clear evidence that reductions in bank capital have important implications for the lending behavior of small banks, there is less direct evidence to support the claim that a capital channel has important implications for the lending behavior of large banks and nonbank financial intermediaries. Nonetheless, a sharp pullback in lending by large commercial banks and nonbank financial institutions during the current financial crisis—owing to lack of liquidity in the interbank funding markets or a retrenchment in lending as these institutions seek to replenish depleted capital—would likely cause a severe slowdown in economic activity by constricting the supply of credit. In particular, the usual mechanisms that allow nonfinancial firms to substitute away from bank loans and other intermediated credit towards arms-length borrowing may become nonoperational in times of a widespread and acute financial distress, especially given the crucial role that the nonbank financial institutions play in originating, marketing, and guaranteeing debt issued by the nonfinancial business sector.

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Corporate Credit Spreads and Economic Activity

Credit spreads have long been used to gauge the degree of strains in the financial system. Because asset prices are forward looking, movements in credit spreads have been shown to be particularly useful for forecasting economic activity.2 Despite some success, results from this strand of research are often sensitive to the choice of a credit spread index under consideration. Moreover, credit spread indexes that contained useful information about macroeconomic outcomes in the past often lose their predictive power for the subsequent cyclical downturn. These mixed results are partly attributable to the rapid pace of financial innovation that likely alters the forecasting power of financial asset prices over time or results in one-off developments that may account for most of the forecasting power of a given credit spread index. To mitigate these problems, Gilchrist, Yankov, and Zakrajˇsek [2008] (GYZ hereafter) rely on the prices of individual senior unsecured corporate debt issues traded in the secondary market to construct a broad array of corporate bond spread indexes that vary across maturity and default risk. Compared with other corporate financial instruments, senior unsecured bonds represent a class of securities with a long history containing a number of 2 The predictive content of various corporate credit spreads for economic activity has been analyzed, among others, by Stock and Watson [1989]; Friedman and Kuttner [1998]; Gertler and Lown [1999]; Mueller [2007]; and King, Levin, and Perli [2007].

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Table 1: Predictive Content of Credit Spreads for Economic Activity (Year-Ahead Forecast Horizon) Real GDP Credit Spread CP1m−Treas1m Aaa−Treas10y Baa−Treas10y HY−Treas10y EDF-Q1 EDF-Q2 EDF-Q3

Employment

Ind. Production

Est.

¯2 R

Est.

¯2 R

Est.

¯2 R

-0.119 [0.98] -0.015 [0.07] -0.081 [0.38] -0.435 [1.49] -0.529 [4.66] -0.588 [4.80] -0.585 [4.81]

0.048 0.044 0.039 0.147 0.320 0.374 0.359 -

-0.258 [2.40] -0.132 [0.56] -0.243 [1.05] -0.687 [3.56] -0.602 [5.90] -0.656 [7.66] -0.653 [8.41]

0.398 0.351 0.370 0.600 0.702 0.763 0.752 -

-0.189 [1.41] -0.194 [0.89] -0.273 [1.30] -0.581 [2.28] -0.646 [5.14] -0.723 [5.65] -0.691 [4.54]

0.132 0.132 0.151 0.298 0.511 0.583 0.517 -

Note: For real GDP, sample period is 1990:Q2–2008:Q2 (T = 69). For private nonfarm employment (EMP) and industrial production (IP), sample period is 1990:II–2008:VII (T = 210). Dependent variables are ∆4 ln(GDPt+4 ), ∆12 ln(EMPt+12 ), and ∆12 ln(IPt+12 ). Each regression specification includes a credit spread, a 4-quarter or a 12-month lag of the respective dependent variable, and a constant term (the latter two effects are not reported) and is estimated by OLS. Estimates of parameters corresponding to credit spreads are standardized; absolute t-statistics reported in brackets are based on a heteroscedasticityand autocorrelation-consistent asymptotic covariance matrix computed according to Newey and West [1987].

business cycles. In addition, the rapid pace of financial innovation has done little to alter the basic structure of these securities. Thus, the information content of spreads constructed from yields on senior unsecured corporate bonds is likely to provide more consistent signals regarding economic outcomes relative to spreads based on securities with a shorter history or securities whose structure or the relevant market has underwent a significant structural change. In addition, GYZ rely on the firm-specific expected default frequencies (EDFs) provided by the Moody’s/KMV corporation to construct their credit spread indexes. Because they are based primarily on observable information in equity markets, EDFs provide a more objective and more timely assessment of firm-specific credit risk compared with the issuer’s senior unsecured credit rating. The results in Table 1 examine the predictive content of various corporate credit spread

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indexes for the following three measures of economic activity: real GDP, private nonfarm payroll employment, and industrial production. Specifically, we estimate a simple forecasting equation in which the year-ahead growth in an indicator of economic activity is regressed on its own value lagged one year and the current value of a credit spread. The entries in the table correspond to the standardized coefficient estimates (with t-statistics in brackets) on the effect of the credit spread on each measure of economic activity, along with the explanatory power of the regression as measured by the adjusted R2 . The first four regressions employ standard credit spread indexes emphasized in this literature.

These include the one month commercial paper Treasury bill spread

(CP1mo−Treas1mo); the Aaa corporate bond spread (Aaa−Treas10y), the Baa corporate bond spread (Baa−Treas10y); and the high-yield corporate bond spread (HY−Treas10y).3 According to the entries in the table, the standard credit spread indexes—with the exception of the high-yield bond spread, which contains substantial explanatory power for the year-ahead growth in employment—contain very little information regarding the future direction in economic activity. The next set of forecasting regressions relies on a subset of the corporate bond spread indexes constructed by GYZ. Specifically, we focus on the credit spread indexes for which GYZ document the highest predictive content, both in and out of sample—that is, credit spreads constructed from very long-maturity bonds (remaining term-to-maturity greater than 15 years) issued by firms in the low- and intermediate-risk categories as defined by the lowest three quintiles of the EDF distribution (EDF-Q1, EDFQ2, and EDF-Q3). Compared with the standard default-risk indicators, the EDF-based credit spread indexes contain significant predictive power for all three measures of economic activity. The coefficients on the EDF-based credit spreads are always statistically and economically significant, and the EDF-based credit spreads generate an in-sample fit that is substantially above that obtained from regressions that rely on the standard credit spread indexes. Table 2 focuses on the predictive content of these credit spread indexes for total business fixed investment spending and its major components—namely, equipment and software (E&S, excluding high tech); high-tech equipment; and nonresidential structures. Again, the paper-bill spread and the credit spread indexes based on Aaa- and Baa-rated long-term corporate bonds have very little explanatory power for total investment spending or its major components. The high-yield bond spread does forecast total investment spending, though not nearly as well as the EDF-based credit spreads. The high-yield spread appears 3

Commercial paper rates are taken from the “Commercial Paper Rates and Outstanding” Federal Reserve statistical release. The source of Treasury yields and yields on Aaa- and Baa-rated corporate bonds is “Selected Interest Rates” (H.15) Federal Reserve statistical release. To construct the high-yield spread, we use the High-Yield Master II index, a commonly used benchmark index for long-term speculative-grade corporate bonds administered by Merrill Lynch.

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Table 2: Predictive Content of Credit Spreads for Business Fixed Investment (Year-Ahead Forecast Horizon) INV-TOT Credit Spread CP1m−Treas1m Aaa−Treas10y Baa−Treas10y HY−Treas10y EDF-Q1 EDF-Q2 EDF-Q3

INV-ES

INV-HT

INV-NRS

Est.

¯2 R

Est.

¯2 R

Est.

¯2 R

Est.

¯2 R

-0.277 [2.32] -0.286 [0.98] -0.334 [1.00] -0.812 [2.94] -0.610 [4.85] -0.656 [5.08] -0.662 [4.71]

0.343 0.343 0.337 0.559 0.653 0.692 0.677 -

-0.291 [2.14] -0.016 [0.08] -0.032 [0.18] -0.357 [1.30] -0.740 [6.49] -0.791 [8.87] -0.827 [11.4]

0.273 0.186 0.187 0.254 0.695 0.721 0.732 -

-0.049 [0.30] -0.393 [1.59] -0.409 [1.33] -0.289 [0.94] -0.506 [3.01] -0.532 [2.95] -0.522 [2.55]

0.337 0.467 0.434 0.387 0.598 0.607 0.581 -

-0.253 [1.66] -0.385 [1.31] -0.455 [1.62] -0.870 [6.15] -0.288 [1.52] -0.351 [1.83] -0.347 [1.69]

0.160 0.231 0.243 0.598 0.182 0.226 0.224 -

Note: Sample period: quarterly data from 1990:Q2 to 2008:Q2 (T = 69). Dependent variables are ∆4 ln(INVt+4 ), where INV denotes real investment spending in the following categories: INV-TOT = total business fixed investment; INV-ES = equipment and software (excluding high tech); INV-HT = high-tech; and INV-NRS = nonresidential structures. Each regression specification includes a credit spread, a 4-quarter lag of the respective dependent variable, and a constant term (the latter two effects are not reported) and is estimated by OLS. Estimates of parameters corresponding to credit spreads are standardized; absolute t-statistics reported in brackets are based on a heteroscedasticityand autocorrelation-consistent asymptotic covariance matrix computed according to Newey and West [1987].

to contain substantial predictive power for investment in nonresidential structures, whereas the EDF-based credit spreads forecast E&S and high-tech investment, but they have limited information content for future expenditures on nonresidential structures. Although not reported, our results indicate that corporate credit spreads have essentially no information content for future consumption spending on both durable and nondurable goods as well as for residential investment. This lack of predictive power holds true for both the standard credit spread indexes and the EDF-based default risk indicators considered by GYZ. Thus, corporate credit spreads do well at predicting business spending but contain little information for household spending. According to Gertler and Lown [1999], the predictive content of credit spreads for economic activity may be due to the presence of an operative financial accelerator mechanism

14

linking balance sheet conditions to the real economy through movements in the external finance premium. As emphasized by Philippon [2008], however, the forecasting ability of credit spreads may also reflect the fact that asset prices contain information about future economic fundamentals in a world without financial market imperfections. In particular, as economic fundamentals deteriorate, default risk will rise, even if defaults impose no additional dead-weight loss on the economy. According to this view, an increase in credit spreads may reflect a reduction in expected future profits during an impending cyclical downturn and does not necessarily provide a causal link by which movements in the external finance premium either amplify economic disturbances originating in the real economy or exert an independent effect on economic activity through financial disruptions that reduce the supply of credit. As argued by Gilchrist, Ortiz, and Zakrajˇsek [2008], one potential way to parse movements in credit spreads between fluctuations owing to financial market imperfections and swings reflecting solely the changes in expected default risk absent financial frictions is to use a pricing model for corporate debt to measure deviations between the current level of spreads and the level of spreads that should prevail assuming the usual pricing of default risk. For example, if default becomes more costly during an economic downturn, one would expect credit spreads to rise relative to an increase in expected default risk. Thus, deviations of credit spreads from those predicted by a standard pricing model may provide direct information about movements in the external finance premium over the business cycle. Table 3 reports the results of such an exercise. In particular, using the monthly panel data set of individual senior unsecured bond issues described in GYZ, we regress the corporate bond spreads in month t on the expected default risk, as measured by the issuer’s year-ahead EDF at the end of month t − 1; the regression specification also includes the bond’s duration and par size to control for term and liquidity premiums. As shown in column 1 of the table, the coefficient on the EDF is economically large and highly statistically significant. Moreover, this reduced-form pricing model explains about 46 percent of the variation in corporate bond spreads. Columns 2 and 3 report regression results that allow for a nonlinear relationship between credit spreads and expected default risk, whereas column 4—in addition to equity-based indicators of default-risk—also includes a full set of credit rating dummies. As evidenced by the entries in the table, allowing for nonlinear effects of expected default risk on credit spreads provides an incremental improvement in the explanatory power of the regression. In contrast, the inclusion credit ratings fixed effects leads to a substantial increase in the goodness of fit. The specification that allows for both nonlinearities in the relationship between credit spreads and expected default risk and credit ratings fixed effects explains almost 62 percent of the variation in corporate bond spreads.

15

Table 3: Corporate Bond Spreads and Expected Default Risk Regression Specification Explanatory Variable

(1)

(2)

(3)

(4)

ln(EDF−1 )2

0.088 (0.016) -0.002 (0.104) 0.362 (0.010) -

ln(EDF−1 )3

-

0.080 (0.016) 0.004 (0.101) 0.428 (0.016) 0.035 (0.005) -

0.459 yes (0.000) no

0.472 yes (0.000) no

0.077 (0.016) 0.005 (0.010) 0.487 (0.015) 0.012 (0.006) -0.012 (0.002) 0.477 yes (0.000) no

0.032 (0.014) 0.050 (0.008) 0.293 (0.012) 0.004 (0.004) -0.008 (0.001) 0.618 yes (0.000) yes (0.000)

ln(PARVALUE) ln(DURATION) ln(EDF−1 )

Adj. R2 Industry Effectsa Ratings Effectsb

Note: Sample period: monthly bond-level data from February 1990 to July 2008 (Obs. = 281,179). Dependent variable is the log of the credit spread in month t. All specifications are estimated by OLS. Asymptotic robust standard errors are clustered at the firm level and are reported in parentheses. a p-values for the test of the null hypothesis of the absence of fixed industry effects are reported in parentheses. b p-values for the test of the null hypothesis of the absence of fixed credit rating effects are reported in parentheses.

We use the reduced-form pricing model in column 4 to construct the residual or pricing error for each bond/month observations in the GYZ data set. We then calculate the average of these pricing errors for each period to obtain an index that essentially removes the average effect of movements in expected default risk on corporate bond spreads. As in Gilchrist, Ortiz, and Zakrajˇsek [2008], we label this index the external finance premium. Figure 4 shows the estimated external finance premium along with, for comparison purposes, the credit spread for very long maturity bonds in the second quintile of the EDF distribution constructed by GYZ (EDF-Q2), and the standard high-yield credit spread index (HY−Treas10y).4 All three series show substantial variation and comovement over the business cycle, although the high-yield spread is considerably more volatile than the other 4

In order to cast the external finance premium index in units that are easily interpretable, we scaled the index such that it is in the same units as the average corporate spread in the GYZ data set.

16

Figure 4: Corporate Bond Spreads and the External Finance Premium Basis points 1200

Monthly

High Yield EDF-Q2 External Finance Premium

1000

800

600

400

200

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Note: The black depicts the high-yield corporate bond spread (HY−Treas10y); the red line depicts the credit spread associated with very long maturity corporate bonds in the second quintile of the EDF distribution (EDF-Q2); and the red line depicts the estimated external finance premium (see text for details). The shaded vertical bars denote NBER-dated recessions.

two credit spread indexes. Focusing on the current period of financial turmoil, the estimated external finance premium shot up in the middle of 2007 and—at just under 400 basis points—is currently substantially above the peak reached at the end of 2000. Thus, although measured default risk may have been significantly higher during the 2001–02 period, the external finance premium is in fact higher in the current period than at any time in the past 19 years. We now examine the extent to which movements in the external finance premium help predict the year-ahead growth in real GDP, private payroll employment, and industrial production. Table 4 contains the results of this exercise; the regression specifications in Table 4 are identical to those reported in Table 1, except that we now use the estimated external finance premium in place of the credit spread indexes. As evidenced by the entries in the table, the forecasting power of the external finance premium is nearly as good as that of the EDF-based credit spread indexes. The coefficient on the external finance premium is highly statistically and economically significant for all three measures of economic activity, and the in-sample fit of all three specifications is almost as good as the highest adjusted R2 reported in Table 4. Overall these results suggest that a significant portion of the predictive power of corporate bond spreads for economic activity likely reflects the information content

17

Table 4: Predictive Content of External Finance Premium for Economic Activity (Year-Ahead Forecast Horizon) Real GDP Explanatory Variable External Finance Premium

Employment

Ind. Production

Est.

¯2 R

Est.

¯2 R

Est.

¯2 R

-0.560 [2.79]

0.294 -

-0.673 [5.64]

0.731 -

-0.733 [3.88]

0.534 -

Note: For real GDP, sample period is 1990:Q2–2008:Q2 (T = 69). For private nonfarm employment (EMP) and industrial production (IP), sample period is 1990:II–2008:VII (T = 210). Dependent variables are ∆4 ln(GDPt+4 ), ∆12 ln(EMPt+12 ), and ∆12 ln(IPt+12 ). Each regression specification includes the estimated external finance premium (see text for details), a 4-quarter or a 12-month lag of the respective dependent variable, and a constant term (the latter two effects are not reported) and is estimated by OLS. Estimates of parameters corresponding to credit spreads are standardized; absolute t-statistics reported in brackets are based on a heteroscedasticity- and autocorrelation-consistent asymptotic covariance matrix computed according to Newey and West [1987].

of credit spreads for disruptions in financial markets or variation in the cost of default, two factors that would cause credit spreads to widen relative to expected default risk prior to an economic downturn.

5

DSGE Models with Financial-Real Linkages

The ability of credit spreads to predict economic activity suggests important linkages between financial conditions and macroeconomic outcomes. Quantifying these linkages, however, requires structural models of the macroeconomy that can distinguish between movements in credit supply and demand and that can account for general equilibrium feedback effects between developments in the financial and real sectors of the economy. Recent work by Christiano, Motto, and Rostagno [2007], Queijo von Heideken [2008], Graeve [2008], and Christensen and Dib [2008] seeks to quantify these mechanisms by estimating dynamic stochastic equilibrium models that incorporate credit market imperfections through the financial accelerator mechanism described in Carlstrom and Fuerst [1997] and Bernanke, Gertler, and Gilchrist [1999] (BGG hereafter).5 Although details differ in terms of model estimation and shock specification, all of these papers document an important role for financial factors in business cycle fluctuations. Queijo von Heideken [2008], for example, shows that the ability of a model with a rich array of real and nominal rigidities to fit both the U.S. and the Euro-area data im5

In an alternative approach, Levin, Natalucci, and Zakrajˇsek [2006] employ firm-level data on credit spreads, EDFs, and leverage to estimate directly the structural parameters of the debt-contracting problem underlying the financial accelerator model of BGG.

18

proves significantly if one allows for the presence of a financial accelerator mechanism; and Christiano, Motto, and Rostagno [2007] demonstrate that shocks to the financial sector have played an important role in economic fluctuations over the past two decades, both in the United States and in Europe. Queijo von Heideken [2008], however, estimates a structural model that is identified without reliance on financial data and that does not allow for shocks to the financial sector, whereas Christiano, Motto, and Rostagno [2007], though allowing for a wide variety of shocks to the financial sector, do not estimate the parameters governing the strength of the financial accelerator mechanism. To date, we are aware of no empirical work that seeks to estimate simultaneously the key parameters of the financial accelerator mechanism along with the shocks to the financial sector. In this section, we briefly summarize the ongoing work by Gilchrist, Ortiz, and Zakrajˇsek [2008] (GOZ hereafter) that attempts to fill this gap. In particular, GOZ use Bayesian maximum likelihood methods to estimates a dynamic New Keynesian model that incorporates the financial accelerator discussed in BGG. The main innovation of their approach is that they incorporate explicitly estimates of the external finance premium constructed from the reduced-form pricing models of corporate debt. These proxies for the unobservable external finance premium are used to identify the strength of the financial accelerator mechanism and to measure the extent to which disruptions in financial markets have contributed to fluctuations in the real economy during the last two decades. For tractability, the model is kept purposefully simple. As in BGG, it allows for a household sector that consumes, saves, and makes labor-supply decisions; an investment goods sector that transforms current output into capital via an adjustment cost mechanism; and a retail sector that faces Calvo-style price rigidities that result in a standard New Keynesian Phillips curve. The model also allows for both habit formation in consumption and for higher-order adjustment costs in investment. These adjustment costs imply that asset prices—the value of capital in place—increase during economic expansions. Monetary policy is conducted by a modified Taylor rule that assumes that the monetary authority, given interest-rate smoothing, adjusts nominal short-term interest rates in response to changes in current inflation and output growth. As in BGG, the model also allows for an entrepreneurial sector that faces significant credit market frictions in the process of owning and operating the existing capital stock. These frictions give rise to an external finance premium that creates a wedge between the required return on capital—the rate at which entrepreneurs can borrow to finance capital accumulation—and the risk-free rate of return received by the household sector for its savings. In this environment, an expansion in output causes an increase in the value of assets in place and a rise in the entrepreneurial net worth. As entrepreneurs’ net worth expands relative to their borrowing, the external finance premium falls, causing a further

19

increase in both asset values and investment demand. These general equilibrium feedback effects, in turn, further amplify the financial accelerator mechanism. The model is estimated using Bayesian maximum likelihood techniques over the period 1985:Q1–2008:Q2, using data on real GDP, business fixed investment, CPI inflation, the nominal federal funds rate, and a risk spread derived from the large panel of issuer-level credit spreads discussed above.6 The estimated model parameters include the degree of habit formation in consumption; adjustment costs to investment; the response of inflation to the output gap in the Phillips curve; and the coefficients that determine the monetary policy rule. GOZ also estimate the elasticity of the external finance premium to changes in net worth, the key parameter governing the strength of the financial accelerator. In addition to the standard set of shocks to household preferences, technology, and monetary policy, the model allows for an exogenous serially-correlated shock to the external finance premium. GOZ interpret this shock as a disturbance to the financial sector that boosts the external finance premium beyond the level warranted by the current economic conditions and the current stance of monetary policy. Consistent with the recent work this area, the estimates of the model parameters indicate an important macroeconomic role for financial market frictions, which act as an amplification mechanism for real and nominal disturbances in the economy. The results also suggests that disturbances that originate in the financial sector have significant real consequences. The left column of Figure 5 depicts the model dynamics in response to a one standard deviation (negative) shock to monetary policy rule, whereas the right column shows the impulse responses to a one standard deviation (negative) shock to the external finance premium. Unanticipated expansionary monetary policy causes an increase in output and investment and a rise in inflation (not shown). It also causes a reduction in the external finance premium, which serves to strengthen the monetary transmission channel through the amplification mechanism described above. Similarly, a reduction in the external finance premium also causes an expansion in investment and output and a fall in inflation (not shown) through the supply-side benefits of increased capital accumulation. The real effect of this mechanism is quantitatively large—a 20 basis point decline in the external finance premium causes an 80 basis point increase in output. To understand the implications of the model for the conduct of monetary policy and to 6

The estimation period uses data prior to 1990, the first year for which monthly estimates of expected default frequencies from Moody’s/KMV are available. Consequently, we are unable to use our proxy for the external finance premium derived from the reduced-form pricing model discussed above. We are currently in the process of estimating the Merton [1974] distance-to-default bond pricing model for the entire nonfinancial corporate sector going back to 1980, which will allows us to construct a similar estimate of the external finance premium over a longer period. For the purposes of this exercise, we constructed the risk spread by extracting the first principal component from credit spreads in a large number of credit-risk portfolios.

20

Figure 5: Model Responses to Selected Shocks Monetary Policy Shock

Financial Shock

Federal funds rate

Risk spread Percentage points

Percentage points 0.1

0.2

0.0

0.1

−0.1

0.0

−0.2 −0.1 −0.3 −0.2

−0.4

−0.3

−0.5 0

4

8

12

16

20

24

28

32

36

40

0

4

8

Quarters after shock

12

16

20

24

28

8

12

16

20

24

28

32

36


1.0

0.4

0.8

0.3

0.6

0.2

0.4

0.1

0.2

0.0

0.0

−0.1

−0.2

40

0

4

8


12

16

20

24

28

8

12

36

40

Investment growth Percentage points

4

32


Investment growth

0

40

Output growth Percentage points

4

36


Output growth

0

32

16

20

24

28

32

36


4

1.5

3

1.0

2

0.5

1

0.0

0

−0.5

−1

40

0


4

8

12

16

20

24

28

32

36

40


Note: The red lines in each panel depicts the estimated impulse responses of selected variables to monetary policy shock (the left column) and to the financial shock (the right column). The shaded bands denote the 80 percent confidence intervals.

evaluate the importance of financial market frictions in determining business cycle outcomes, we calculate the portion of the movements in the actual growth of output and investment, nominal federal funds rate, and the risk spread that can be accounted for by monetary policy innovations and shocks to the supply of credit. Figures 6–7 contain the result of this exercise. Each panel of these two figures, shows the actual series—in percentage point deviations from steady state—along with the estimated contribution of the two shocks. Figure 6 summarizes the effects of a monetary policy shock, whereas Figure 7 focuses on 21

Figure 6: Historical Decomposition of Monetary Policy Shocks Output growth Percentage points 3

Actual Contribution of monetary policy shock

2 1 0 −1 −2 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Investment growth Percentage points 15 10 5 0 −5 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Federal funds rate Percentage points 4 3 2 1 0 −1 −2 −3 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Risk spread Percentage points 1.0 0.5 0.0 −0.5 −1.0 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Note: The solid black lines in each panel depicts the behavior of actual variables expressed in percentage point deviations from the steady state. The dotted red line in each panel depict the estimated effect of monetary policy shocks (see text for details). The shaded vertical bars denote NBER-dated recessions.

22

Figure 7: Historical Decomposition of Financial Shocks Output growth Percentage points 4

Actual Contribution of financial shock

3 2 1 0 −1 −2

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Investment growth Percentage points 15 10 5 0 −5 −10 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Federal funds rate Percentage points 4 3 2 1 0 −1 −2 −3 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Risk spread Percentage points 2.0 1.5 1.0 0.5 0.0 −0.5 −1.0 1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

Note: The solid black lines in each panel depicts the behavior of actual variables expressed in percentage point deviations from the steady state. The dotted red line in each panel depict the estimated effect of financial shocks (see text for details). The shaded vertical bars denote NBER-dated recessions.

23

the financial shock. As shown in the four panels of Figure 6, the effect of monetary policy shocks on the economy accord well with the historical record regarding the conduct of monetary policy since the mid-1980s. Monetary policy was tight in the late 1980s prior to the onset of 1990– 91 recession but was eased substantially during the economic downturn of the early 1990s. According to our estimates, tight monetary policy also contributed to the slowdown in business investment and output during the 1994–95 period. The stance of monetary policy was roughly neutral up through the collapse of the stock market in early 2000, and according to our estimates, policy was eased significantly during the 2001 recession. Monetary policy was again relatively tight during the housing boom of the 2005–07. The rapid sequence of cuts in the federal funds rate during 2007 also appears as a significant easing of monetary conditions that has supported expansion in investment and output during that period. An appealing feature of this model is that the monetary transmission mechanism works in part through its impact on balance sheet conditions—that is, the external finance premium is strongly countercyclical in response to monetary policy shocks. The estimated effects of financial disturbances and their impact on the real economy also accord well with historical perceptions of the likely effects of tight credit conditions on economic activity. According to our estimates, the economy showed signs of financial distress at the onset of the 1990–91 recession, and adverse financial conditions remained a drag on the real economy throughout the “jobless” recovery of the early 1990s. Indeed, between 1989 and 1993, shocks to the financial sector caused the external finance premium to rise by 150 basis points, an increase that led to an extended period of subpar economic performance. Credit conditions improved markedly during the second half of the 1990s, a period during which the external finance premium fell about 250 basis points. The premium moved higher after the bursting of the “dot-com” bubble, and financial conditions deteriorated further at the onset of the collapse in the housing sector in 2005. The model also captures the current financial crisis as a shock to the financial sector, manifested as a 75 basis point jump in the external finance premium that has led to a sharp slowdown in the growth of investment and output during the last four quarters. In summary, this relatively simple model of the financial accelerator—when estimated using both real and financial market data—does remarkably well at capturing much of the historical narrative regarding the conduct of monetary policy and developments in financial markets that led to episodes of financial excess and distress over the last two decades. Despite this apparent success, it would be undoubtedly useful to expand the current model to incorporate additional real-side and nominal frictions such as variable capacity utilization and sticky nominal wages, combined with wage and price indexation. Another useful direction of this line of research would be to enrich the financial sector by introducing frictions

24

in the intermediation process that links household borrowing to house prices as in Aoki, Proudman, and Vlieghe [2004] and Iacoviello [2005] and by explicitly modeling the financial sector and the role that financial capital may have on the real economy. These extensions of the basic framework are necessary both for empirical realism and to capture what are undoubtedly the main concerns of policymakers today, namely the ongoing collapse of house prices and its impact on the real economy through its effect on the value of assets held by financial institutions.

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