This is the first study that computes default measures for individual firms using Merton’s (1974) option pricing model, to assess the effect that default risk has on equity returns. We find that equally-weighted portfolios of stocks with high default probability earn significantly higher returns than equally-weighted portfolio of stocks with low default probability. In addition, both the size and book-to-market effects are present only within the portfolio of stocks with the highest default probabilities. Once stocks with the 30 % highest default probabilities are excluded from the sample, both size and B/M effects disappear. We also find that default risk is priced and can explain part of the cross-sectional variation in returns. The Fama-French factors SMB and HML, and particularly SMB, contain some default-related information, although it appears that this information is not the driving force behind the success of the Fama-French model. Keywords: default risk, equity returns, Merton’s (1974) model, size and book-to-market. JEL classification: G33, G12 1

wish to thank Andre Haris, Lozan Bakayatov, and Davron Yakubov for their excellent data collection efforts. In addition, we thank the financial assistance of the Social Sciences and Humanities Research Council of Canada. All errors remain the responsibility of the authors. Corporate Yield Spreads and Bond Liquidity We examine whether liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4000 corporate bonds and spanning investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads; and that an improvement of liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond-specific, firm-specific, and macroeconomic variables, and are robust to issuers ’ fixed effect and potential endogeneity bias. Our finding mitigates the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants, and suggests that liquidity plays an important role in corporate bond valuation.

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We examine the accuracy and contribution of the default forecasting model based on Merton’s (1974) bond pricing model and developed by the KMV corporation. Comparing the KMV-Merton model to a similar but much simpler alternative, we find that it performs slightly worse as a predictor in hazard models and in out of sample forecasts. Moreover, several other forecasting variables are also important predictors, and fitted hazard model values outperform KMV-Merton default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with KMV-Merton default probabilities after adjusting for agency ratings, bond characteristics, and our alternative predictor. We conclude that the KMV-Merton model does not produce a sufficient statistic for the probability of default, and it appears to be possible to construct such a sufficient statistic without solving the simultaneous nonlinear equations required by the KMV-Merton model. We include the SAS code we use to calculate KMV-Merton default probabilities in an appendix.

How did the Subprime Crisis, a problem in a small corner of U.S. financial markets, affect the entire global banking system? To shed light on this question we use principal components analysis to identify common factors in the movement of banks ’ credit default swap spreads. We find that fortunes of international banks rise and fall together even in normal times along with short-term global economic prospects. But the importance of common factors rose steadily to exceptional levels from the outbreak of the Subprime Crisis to past the rescue of Bear Stearns, reflecting a diffuse sense that funding and credit risk was increasing. Following the failure of Lehman Brothers, the interdependencies briefly increased to a new high, before they fell back to the pre-Lehman elevated levels – but now they more clearly reflected heightened funding and counterparty risk. After Lehman’s failure, the prospect of global recession became imminent, auguring the further deterioration of banks ’ loan portfolios. At this point the entire global financial system had become infected. 1

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We develop a structural bond valuation model to simultaneously capture liquidity and credit risk. Our model implies that renegotiation in financial distress is influenced by the illiquidity of the market for distressed debt. As default becomes more likely, the components of bond yield spreads attributable to illiquidity increase. When we consider finite maturity debt, we find decreasing and convex term structures of liquidity spreads. Using bond price data spanning 15 years, we find evidence of a positive correlation between the illiquidity and default componentsofyieldspreadsaswellassupportfordownward-slopingtermstructuresof liquidity spreads. Credit risk and liquidity risk have long been perceived as two of the main justifications for the existence of yield spreads above benchmark Treasury notes or bonds (see Fisher (1959)). Since Merton (1974), a rapidly growing body of literature has focused on credit risk. 1 However, while concern about market liquidity issues has become increasingly marked since the autumn of 1998, 2 liquidity remains a relatively unexplored topic, in particular, liquidity for defaultable securities. 3 This paper develops a structural bond pricing model with liquidity and credit risk. The purpose is to enhance our understanding of both the interaction between these two sources of risk and their relative contributions to the yield spreads on corporate bonds. Throughout the paper, we define liquidity as the ability to sell a security promptly and at a price close to its value in frictionless markets, that is, we think of an illiquid market as one in which a sizeable discount may have to be incurred to achieve immediacy. We model credit risk in a framework that allows for debt renegotiation as in Fan and Sundaresan (2000). Following François and Morellec (2004), we also introduce

and the Workshop on Capital Structure Arbitrage at the University of Evry. We are particularly grateful for the comments and suggestions of Jun Liu, the Editor We conduct an analysis of the risk and return characteristics of a number of widelyused fixed income arbitrage strategies. We find that the strategies requiring more “intellectual capital ” to implement tend to produce significant alphas after controlling for bond and equity market risk factors. These positive alphas remain significant even after taking into account typical hedge fund fees. In contrast with other hedge fund strategies, many of the fixed income arbitrage strategies produce positively skewed returns. These results suggest that there may be more economic substance to fixed income arbitrage than simply “picking up nickels in front of a steamroller.

This paper provides new evidence on the empirical success of structural models in explaining corporate credit risk changes. A parsimonious set of common factors and firm-level fundamentals, inspired by structural models, explains more than 54 % (67%) of the variation in credit spread changes for medium (low) grade bonds. No dominant latent factor is present in the unexplained variation. While our set of variables has lower explanatory power among high-grade bonds, it does capture most of the systematic variation of credit-spread changes in that category as well. It also subsumes the explanatory power of the Fama and French (1993) factors among all grade classes. This paper assesses the success of structural models in empirical studies of changes in corporate credit spreads. Our focus is set on the change in the credit spread, not its level. 1 The difference in studying credit spreads vs. changes in credit spreads is equiv-alent to the difference in studying equity prices vs. equity expected returns. Indeed, there is a one-to-one correspondence between the spread level and the bond price, while

We use information contained in yield spreads to recover investors ’ ex ante required rates of return on corporate securities, and then use these ex ante returns to study the pricing of risky assets. Differently from the standard approach, our asset pricing tests do not rely on the use of ex post average equity returns as proxies for expected equity returns. We find that: (i) the market beta plays a significant role in the cross-section of expected equity returns, and its role persists even after size and book-to-market factors are accounted for; (ii) the risk premia associated with size and book-to-market are positive, significant, and countercyclical; and (iii) there is little evidence on positive momentum profits. We also find that systematic risk, as captured by common equity factors, is the main driver of the cross-sectional variation in bond yield spreads. JEL Classification: G12, E44