Abstract
We investigate changes in the risk-relevance of securitized subprime, other nonconforming, and commercial mortgages for sponsor-originators during the recent financial crisis. Using the volatility of realized stock returns, option-implied volatility, and credit spreads, we observe a pronounced increase in the risk-relevance of subprime securitizations as early as 2006. Furthermore, reflecting the evolution of the financial crisis in waves, we find that investors recognized the increased credit risk of other nonconforming and commercial mortgage securitizations as the financial crisis progressed. Additional analyses show that risk-relevance varies cross-sectionally with structural characteristics such as monoline credit-enhancement and the presence of special servicers for commercial mortgage securitizations. Our results inform the current debates on the opacity of securitization structures and highlight the need to take into account cross-sectional and inter-temporal heterogeneity in risk-relevance across securitized asset classes and securitization characteristics (e.g., quality and type of collateral and transaction structure).
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Notes
Data from the Securities Industry and Financial Markets Association (SIFMA) shows that total issuance of asset backed securities in the US increased from $202 billion in 1997 to $754 billion in 2006. SIFMA has made this data available at http://www.sifma.org/research/statistics.aspx.
Consistent with prior literature on risk-relevance of off-balance sheet positions (e.g., Bowman 1980; Dhaliwal 1986; Ely 1995; Niu and Richardson 2006; Chen et al. Ryan 2008; Barth et al. 2012), we consider securitized assets to be risk-relevant if they are associated with the equity or credit risk of the S–Os (see also Ryan 2012). Specifically, we are interested in whether the equity or credit risk of the S–Os is explained by the extent of securitized assets’ credit risk retained by the S–Os.
Note that we do not make any claims or assumptions about the extent of market efficiency. In other words, our results do not speak to whether the capital market assessment of credit-risk retention related to securitized assets was adequate or accurate.
See for example, Keoun (2008).
Our study also relates to recent subprime crisis-related lawsuits. For example, in 2011, the Bank of America proposed an $8.5 billion settlement with various securitization parties including investors in the asset-backed securities. Our results do not support the claims that market participants were completely unaware of the increasing credit risk in subprime mortgages as the crisis approached.
Note that we are interested in the waves of the financial crisis as it related to declines in values of asset classes used as collateral in mortgage securitizations. We refer the reader to Gorton and Metrick (2012) for guidance on the more general economy-wide evolution of the crisis.
Monoline insurance companies were traditionally in the business of insuring investors from losses in the municipal bonds market but forayed into structured credit instruments before the financial crisis. Major monoline insurance companies included MBIA, FSA, FGIC, and AMBAC.
Another way to derive similar predictions appeals to the finance asset-pricing literature, which documents a positive relation between equity volatility and financial leverage (e.g., Christie 1982, Schwert 1989, and Aydemir et al. 2007). Given that most securitization structures are thinly capitalized, the S/A ratio can be viewed as analogous to an off-balance sheet leverage ratio. The simplest form of such a specification follows Christie (1982), who documents a positive relation between leverage and equity volatility. With further simplifying assumptions, the coefficient on leverage can be written as a positive function of the underlying asset volatility. Thus both this approach and our approach lead to the same prediction: the risk-relevance coefficient on S/A increases as the underlying asset volatility (or in other words, the riskiness of the underlying asset collateral) increases.
The definition of STDRET i,t+1 follows Chen et al. (2008).
Note that this time window includes the potential effects of prepayments. Accordingly, we have not adjusted for this further.
In a sensitivity test, we have repeated the analysis based on varying average contractual maturities by asset class and obtained qualitatively similar results.
Untabulated analyses indicate that the results are similar if we omit this control variable.
Increase in delinquency rates implies higher credit devaluation in the corresponding asset class. Note that, while the delinquency rates for commercial mortgages are available for the entire sample period, the Bloomberg Subprime and Alt-A indices are computed by Bloomberg only from 2005 onwards. Accordingly, our cumulative percentage change measures for subprime and nonconforming mortgages (DEV_SPMBS t and DEV_NCMBS t ) are assigned zero values prior to 2005.
Specifically, \( PD_{i,t + 1} = f(SPREAD_{i,t + 1} ) = 1 - \left( {1 - N\left\{ {N^{ - 1} \left[ {(1 - e^{{ - SPREAD_{i,t + 1} \times T}} )/(1 - R)} \right] - \lambda \sqrt {r_{i,t + 1}^{2} } \sqrt T } \right\}} \right)^{\frac{1}{T}} \).
Specifically, see Eq. (11) in Correia et al. (2012).
Our results are robust to Barth et al.’s (2012) approach where bond spreads are regressed on explanatory variable directly using OLS.
The Asset-Backed Alert database is generally accessible to subscribers of Harrison Scott Publications’s popular industry newsletter. The data have been used by influential regulatory studies such as the Board of Governors of the Federal Reserve System Report (2010).
SFAS 140 is effective for fiscal years beginning after December 31, 2000, with fiscal 2000 being the transition year. Our sample period includes fiscal 2000, since many firms chose to provide SFAS 140 disclosures voluntarily during fiscal 2000. For example, Washington Mutual disclosed in its 10-Qs retained interests from the first quarter of 2000 onwards. Our results and inferences are not sensitive to excluding firm quarter observations from fiscal 2000.
In our sample, retained interests disclosure could only be found for 1,513 firm-quarters, which account for 41.1 % of the total observations. For those interim quarter observations for which we could not find retained interests disclosure in firms’ quarterly reports, we assign the value from the most recent annual report.
For SPMBS i,t , NCMBS i,t , CMBS i,t , OTHBS i,t , and RI i,t , the descriptives are provided for firm-quarters that have nonzero values. For the remaining variables, the descriptive are provided for all firm quarters with available data.
Note that, before partitioning by collateral type, the mean of our cumulative securitization measure (CUMOBS i,t ) is 0.431, or 43 % of the total assets of the firm.
The correlations are calculated using all available firm-quarter data. For SPMBS i,t , NCMBS i,t , CMBS i,t , OTHBS i,t , and RI i,t , the correlations are calculated including all the zero values.
In Panel B of Table 2, the Pearson correlation coefficient between STDRET i,t+1 and CMBS i,t is negative, which is opposite to our expected sign. We have confirmed that this is due to the positive correlation between CMBS i,t and LOGMV i,t . In a simple regression analysis that regresses STDRET i,t+1 on LOGMV i,t and CMBS i,t , we find that the association between STDRET i,t+1 and CMBS i,t is positive after controlling for LOGMV i,t .
An F test rejects the null that the coefficients are jointly equal to each other (p value < 0.001).
We have evaluated the plausibility of the regression coefficients if one were to assume the relation between leverage and equity volatility in Christie (1982, Eq. (5)). We find that coefficient estimates are plausible given the empirical parameters observed in our sample. In particular, we find that substituting our regression coefficients and sample bond spreads in the Christie (1982) model provides estimates of asset volatility that are quite comparable to our sample equity volatility. The calculations are available from the authors upon request.
The variance inflation factors for Table 3 are less than 4, mitigating concerns about multi-collinearity.
It is uncommon for commercial mortgage securitizations to have monoline credit enhancement.
We have also conducted F tests to test our risk-transfer predictions in the levels. For example, with regards to subprime securitizations, for 2006, 2007 and 2008, we use appropriate F tests to evaluate the null hypothesis that the level of risk-relevance is zero given the presence of monoline insurance. For 2006 and 2007, we obtain insignificant F test p values, suggesting no risk-relevance for insured S–Os. However, the corresponding p values for 2008 and 2009 are 0.020 and 0.038 respectively, which rejects the null of zero risk-relevance for monoline-insured subprime securitizations. We observe similar patterns for nonconforming mortgages (i.e., monoline insurance appears to be effective in 2007 but not so in 2008 and 2009). Thus our key inferences hold not only in the shifts but the levels as well. Details are available upon request.
Idiosyncratic volatility is calculated as the standard deviation of the residuals from a regression of stock returns on value-weighted market returns for each subsequent firm-quarter.
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Acknowledgments
Work on this paper was partly completed while Dushyantkumar Vyas was at University of Minnesota. The authors thank the editor (Scott Richardson), an anonymous reviewer, Dan Amiram, Joy Begley, Jeffrey Callen, Robert Herz, Giri Kanagaretanam, Tom Linsmeier, Peter Martin, Michel Magnan, Marcia Mayer, Miguel Minutti, Jeffrey Ng, Flora Niu, Sugata Roychowdhary, Stephen Ryan, Catherine Shakespeare, Dan Taylor, Eric Weisbrod, Jim Wahlen, Paul Zarowin, and workshop participants at the University of Alberta Accounting Research Conference (Banff), the 2011 Columbia Burton Conference, the 2011 meetings of the American Accounting Association and the Canadian Academic Accounting Association, Chinese University of Hong Kong, Concordia University, the JCAE Conference (Hong Kong), University of Miami, NERA Economic Consulting, and the University of Toronto for helpful comments this paper. We thank Florin Vasvari for help in computing yield spreads in the primary and secondary bond markets. Gordon Richardson thanks KPMG for its generous financial support.
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Appendices
Appendix 1: Variable definitions
Variable | Definition (compustat data items in parentheses) | Data source | ||
|---|---|---|---|---|
Dependent variables | ||||
STDRET i,t+1 | Standard deviation of daily stock returns measured over the subsequent quarter | CRSP | ||
IMPV91 i,t+1 | The average daily option-implied volatility measured over the subsequent quarter (calculated using standardized at-the-money puts and calls options with 91 days duration) | OptionMetrics | ||
N −1 [f(SPREAD i,t+1 )] | A nonlinear functional transformation of SPREAD i,t+1 . SPREAD i,t+1 is the weighted average yield for new bonds issued during the subsequent quarter, minus the yield on US Treasury bills with closest corresponding maturity. If a firm has multiple bonds, we calculate the average yield weighted by principal amount. The functional transformation of SPREAD i,t+1 is described in Sect. 3 | Mergent FISD | ||
N −1 [f(SPREAD2 i,t+1 )] | A nonlinear functional transformation of SPREAD2 i,t+1 . SPREAD2 i,t+1 is the weighted average yield for bonds traded in the secondary market during the subsequent quarter, minus the yield on US Treasury bills with closest corresponding maturity. If a firm has multiple bonds, we calculate the average yield weighted by principal amount. The functional transformation of SPREAD2 i,t+1 is described in Sect. 3 | TRACE | ||
DISPERSION i,t+1 | Equity analysts’ earnings forecast dispersion, calculated as the coefficient of variation of analysts’ estimates of one-year-ahead annual earnings during the subsequent quarter’s last month | I/B/E/S | ||
Securitization variables | ||||
SPMBS i,t | The total dollar amount of subprime mortgage-backed securities issued over the 20 quarters prior to and including the current quarter, scaled by total assets (ATQ) | Asset-Backed Alert | ||
NCMBS i,t | The total dollar amount of other nonconforming mortgage-backed securities issued over the 20 quarters prior to and including the current quarter, scaled by total assets (ATQ). Other nonconforming mortgages include nonagency residential mortgages (including Alt-A), high loan-to-value loans, nonperforming mortgages, home-equity loans, home-improvement loans, and home-equity lines of credit | Asset-Backed Alert | ||
CMBS i,t | The total dollar amount of commercial mortgage-backed securities issued over the 20 quarters prior to and including the current quarter, scaled by total assets (ATQ) | Commercial Mortgage Alert | ||
OTHBS i,t | The total dollar amount of other (nonmortgage) asset-backed securities issued over the 20 quarters prior to and including the current quarter, scaled by total assets (ATQ). Other assets include credit card receivables, aircraft-lease receivables, auto loans, boat loans, equipment loans, etc. | Asset-Backed Alert | ||
CUMOBS i,t | The sum of SPMBS i,t , NCMBS i,t , CMBS i,t , and OTHBS i,t | |||
Interaction test variables | ||||
MNLSP i,t | Variable indicating if the majority (at least 50 %) of the outstanding subprime issues (issued during the 20 quarters prior to and including the current quarter) were credit-enhanced by a guarantee from a monoline bond insurance company | Asset-Backed Alert | ||
MNLNC i,t | Variable indicating whether the majority (at least 50 %) of the outstanding other nonconforming issues (issued during the 20 quarters prior to and including the current quarter) were credit-enhanced by a guarantee from a monoline bond insurance company | Asset-Backed Alert | ||
SPSERV i,t | Variable indicating whether, for the majority (at least 50 %) of the outstanding commercial mortgage issues (issued during the 20 quarters prior to and including the current quarter), the sponsor and the special servicer were the same entity | Commercial Mortgage Alert | ||
DEV_SPMBS t | Cumulative percentage change in the Bloomberg 60 + day delinquency index for subprime mortgages from the beginning of 2005 to the end of quarter t. Zeros are assigned to quarters prior to 2005 | Bloomberg | ||
DEV_NCMBS t | Cumulative percentage change in the Bloomberg 60 + day delinquency index for Alt-A mortgages from the beginning of 2005 to the end of quarter t. Zeros are assigned to quarters prior to 2005 | Bloomberg | ||
DEV_CMBS t | Cumulative percentage change in commercial mortgage delinquency rates reported by the Federal Reserve from the beginning of 2000 to the end of quarter t. (http://www.federalreserve.gov/releases/chargeoff/) | Board of Governors of Federal Reserve System | ||
Control variables | ||||
DISP i,t | Equity analysts’ earnings forecast dispersion, calculated as the coefficient of variation of analysts’ estimates of one-year-ahead annual earnings during each quarter’s last month | I/B/E/S | ||
LOGMV i,t | The natural logarithm of the firm’s market value of equity (PRCCQ × CSHOQ) | Compustat | ||
STDEPS i,t | The coefficient of variation of earnings per share excluding extraordinary items (EPSPXQ) over the 20 quarters prior to and including the current quarter | Compustat | ||
LEV i,t | The leverage ratio, calculated as total liabilities (LTQ) divided by total assets (ATQ). For banks, we deduct deposits (DPTCQ) from total liabilities to calculate LEV | Compustat | ||
VIX t | The Chicago Board Options Exchange S&P 500 Volatility Index at each quarter end | Datastream | ||
RET0609 i | Cumulative stock returns for each firm from 2006 to 2009 | CRSP | ||
RI i,t | Retained interests, deflated by total assets (ATQ) at the fiscal quarter-end | SEC filings | ||
MATURITY i,t+1 | The number of years to maturity for new bonds issued during the subsequent quarter. If a firm has multiple bonds, we calculate the average maturity weighted by principal amount | Mergent FISD | ||
LOGAMT i,t+1 | The natural log of the total principal amount of new bonds issued during the subsequent quarter | Mergent FISD | ||
NUMCOV i,t+1 | The weighted average number of covenants for new bonds issued during the subsequent quarter. We calculate the average number of covenants weighted by principal amount | Mergent FISD | ||
MATURITY2 i,t+1 | The number of years to maturity for bonds traded in the secondary market during the subsequent quarter. If a firm has multiple bonds, we calculate the average maturity weighted by principal amount | Mergent FISD | ||
LOGAMT2 i,t+1 | The natural log of the total principal amount of bonds traded in the secondary market during the subsequent quarter | Mergent FISD | ||
COUPON2 i,t+1 | The weighted average coupon rate of the bonds traded in the secondary market during the subsequent quarter. We calculate the average coupon rate weighted by principal amount | Mergent FISD | ||
NUMCOV2 i,t+1 | The weighted average number of covenants of the bonds traded in the secondary market during the subsequent quarter. We calculate the average number of covenants weighted by principal amount | Mergent FISD | ||
Industry indicators | Based on industry classification in Barth et al. (2008) | |||
Year indicators | Indicator variables for the years | |||
Appendix 2: Subprime mortgage securitization (in $ millions)
Sponsor | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Countrywide | 0 | 4,281 | 2,215 | 3,933 | 3,233 | 4,939 | 4,425 | 37,993 | 34,967 | 26,345 | 17,401 | 171 | 139,902 |
Lehman brothers | 0 | 0 | 3,575 | 5,383 | 1,282 | 5,793 | 4,055 | 5,883 | 10,219 | 13,742 | 13,088 | 3,440 | 66,461 |
Washington mutual | 1,233 | 0 | 0 | 1,491 | 10,838 | 3,079 | 900 | 10,201 | 12,476 | 6,552 | 5,877 | 0 | 52,647 |
Bear stearns | 459 | 115 | 600 | 1,084 | 1,340 | 2,036 | 4,416 | 4,797 | 6,373 | 6,495 | 8,576 | 0 | 36,292 |
Goldman sachs | 0 | 0 | 0 | 0 | 0 | 4,314 | 2,538 | 8,096 | 7,179 | 7,470 | 6,460 | 0 | 36,058 |
Morgan Stanley | 0 | 0 | 0 | 0 | 1,459 | 5,433 | 1,605 | 5,250 | 0 | 4,291 | 13,863 | 0 | 31,900 |
Citigroup | 0 | 0 | 0 | 0 | 0 | 1,003 | 5,175 | 519 | 1,255 | 5,507 | 10,778 | 0 | 24,239 |
J. P. Morgan chase | 0 | 0 | 0 | 0 | 0 | 433 | 6,335 | 2,453 | 1,435 | 5,977 | 6,465 | 0 | 23,099 |
New century financial | 0 | 3,167 | 2,340 | 1,006 | 3,941 | 1,782 | 1,566 | 0 | 6,442 | 313 | 0 | 0 | 20,557 |
Bank of America | 0 | 0 | 0 | 0 | 0 | 1,381 | 662 | 5,979 | 7,863 | 2,682 | 1,838 | 0 | 20,406 |
Deutsche Bank | 0 | 0 | 0 | 0 | 1,048 | 1,871 | 295 | 1,752 | 1,393 | 3,062 | 6,895 | 0 | 16,317 |
Impac | 0 | 0 | 252 | 944 | 1,158 | 2,676 | 5,372 | 5,887 | 0 | 0 | 0 | 0 | 16,289 |
Wells Fargo | 0 | 0 | 0 | 133 | 0 | 342 | 0 | 6,271 | 4,686 | 2,755 | 983 | 0 | 15,171 |
Barclays | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,387 | 528 | 3,443 | 7,583 | 0 | 12,941 |
IndyMac | 0 | 0 | 0 | 0 | 0 | 135 | 0 | 2,316 | 3,784 | 1,665 | 2,244 | 0 | 10,145 |
Banco popular | 0 | 125 | 195 | 190 | 672 | 0 | 0 | 1,321 | 3,702 | 1,578 | 0 | 0 | 7,783 |
Novastar financial | 264 | 0 | 0 | 0 | 1,197 | 1,224 | 0 | 0 | 0 | 1,234 | 3,186 | 0 | 7,105 |
Fieldstone investment | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4,296 | 750 | 1,011 | 358 | 0 | 6,416 |
ECC capital | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5,029 | 0 | 0 | 0 | 5,029 |
American home mortgage | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,731 | 1,754 | 0 | 3,485 |
Advanta | 0 | 376 | 1,243 | 1,050 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,668 |
WMC finance | 0 | 1,896 | 236 | 406 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,538 |
Ocwen financial | 0 | 1,618 | 399 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 81 | 0 | 2,098 |
Dynex capital | 0 | 1,574 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,574 |
Thornburg mortgage | 0 | 1,144 | 0 | 150 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,294 |
East West Bank | 0 | 0 | 0 | 0 | 0 | 160 | 0 | 0 | 0 | 513 | 386 | 0 | 1,059 |
Ryland | 0 | 0 | 1,047 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,047 |
Newcastle investments | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,036 | 0 | 1,036 |
PNC | 968 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 968 |
Equity one | 0 | 0 | 0 | 0 | 0 | 427 | 0 | 0 | 0 | 0 | 454 | 0 | 881 |
Superior Bank | 0 | 750 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 750 |
Republic leasing | 191 | 170 | 250 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 611 |
Compass Bank | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 591 | 0 | 0 | 0 | 0 | 591 |
Centex | 0 | 0 | 572 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 572 |
Radian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 99 | 281 | 0 | 0 | 379 |
SunTrust | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 371 | 0 | 371 |
Hanover capital mortgage | 0 | 102 | 239 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 360 |
Provident Bank | 0 | 350 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 350 |
Capstead | 73 | 0 | 0 | 230 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 304 |
Zions first national | 0 | 0 | 0 | 0 | 277 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 277 |
Union planters | 0 | 0 | 132 | 127 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 260 |
Ocean Bank | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 190 | 0 | 0 | 190 |
Donaldson, Lufkin & Jenrette | 22 | 0 | 96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 118 |
ITLA Capital | 0 | 0 | 0 | 0 | 0 | 86 | 0 | 0 | 0 | 0 | 0 | 0 | 86 |
Apex mortgage | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
Total | 3,238 | 15,668 | 13,391 | 16,145 | 26,446 | 37,112 | 37,345 | 104,993 | 108,181 | 96,838 | 109,680 | 3,611 | 572,650 |
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Dou, Y., Liu, Y., Richardson, G. et al. The risk-relevance of securitizations during the recent financial crisis. Rev Account Stud 19, 839–876 (2014). https://doi.org/10.1007/s11142-013-9265-4
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DOI: https://doi.org/10.1007/s11142-013-9265-4