This study empirically examines the value added for investors during the 2007–2009 financial crisis from hedge fund-like equity mutual funds, including 130/30, market neutral, and long/short equity funds. We find that based on the information ratio, all market neutral funds, top 90% of long/short funds, and top 25% of 130/30 funds outperform a long-only passive index fund over the crisis period. However, we find little evidence of abnormal performance by the average and median funds in our sample, based on either unconditional or conditional four-factor alphas. The reason for the overall under-performance in the crisis period is that while short positions taken by these funds do generate alpha, the gain from their short positions is not sufficiently large to offset the loss from their long positions. Finally, the abnormal performance of short positions is found to be attributable to managers’ characteristic-adjusted and industry-adjusted stock selection skills. One implication of this study is that even though market neutral and long/short funds on average may not generate alpha, investors can benefit from holding these funds, especially the former, that can provide a hedge against down markets due to their low betas and that can be useful for asset allocation.

This paper examines the link between ownership concentration and corporate bond volatility. We show that more concentrated mutual fund ownership is associated with higher volatility of corporate bonds. This relation is stronger among more illiquid bonds, during periods of heightened bond market illiquidity, and among bonds held by corporate bond funds that invest in more illiquid bonds and experience higher or more correlated liquidity shocks. Using a sample of mutual fund mergers, we further show that increases in bond volatility are unlikely to be driven entirely by the endogenous ownership structure of corporate bonds. Our findings suggest that the concentrated ownership by corporate bond mutual funds provides another channel, apart from illiquidity, to help explain the excess volatility in corporate bonds.

Empirical studies of structural credit risk models so far are often based on calibration, rolling estimation, or regressions. This paper proposes a GMM-based method that allows us to estimate model parameters and test model-implied restrictions in a unified framework. We conduct a specification analysis of five representative structural models based on the proposed GMM procedure, using information from both equity volatility and the term structure of single-name credit default swap (CDS) spreads. Our test results strongly reject the Merton (1974) model and two diffusion-based models with a flat default boundary. The other two models, one with jumps and one with stationary leverage ratios, do improve the overall fit of CDS spreads and equity volatility. However, all five models have difficulty capturing the dynamic behavior of both equity volatility and CDS spreads, especially for investment-grade names. On the other hand, these models have a much better ability to explain the sensitivity of CDS spreads to equity returns.

We present a comprehensive analysis to calculate the Basel III liquidity coverage ratio (LCR) and the net stable funding ratio (NSFR) of U.S. commercial banks using Call Report data over the period 2001–2011, and provide indirect empirical evidence on net cash outflow rates of certain liability categories. In addition, we examine potential links between Basel III liquidity risk measures and bank failures using a model that differentiates between idiosyncratic and systemic liquidity risks. We find that while both the NSFR and the LCR have limited effects on bank failures, the systemic liquidity risk is a major contributor to bank failures in 2009 and 2010. This finding suggests that an effective framework of liquidity risk management needs to target liquidity risk at both the individual level and the system level.

This article empirically tests five structural models of corporate bond pricing: those of Merton (1974), Geske (1977), Longstaff and Schwartz (1995), Leland and Toft (1996), and Collin-Dufresne and Goldstein (2001). We implement the models using a sample of 182 bond prices from firms with simple capital structures during the period 1986--1997. The conventional wisdom is that structural models do not generate spreads as high as those seen in the bond market, and true to expectations, we find that the predicted spreads in our implementation of the Merton model are too low. However, most of the other structural models predict spreads that are too high on average. Nevertheless, accuracy is a problem, as the newer models tend to severely overstate the credit risk of firms with high leverage or volatility and yet suffer from a spread underprediction problem with safer bonds. The Leland and Toft model is an exception in that it overpredicts spreads on most bonds, particularly those with high coupons. More accurate structural models must avoid features that increase the credit risk on the riskier bonds while scarcely affecting the spreads of the safest bonds.

In this article, we present a new method for pricing and hedging American options along with an efficient implementation procedure. The proposed method is efficient and accurate in computing both option values and various option hedge parameters. We demonstrate the computational accuracy and efficiency of this numerical procedure in relation to other competing approaches. We also suggest how the method can be applied to the case of any American option for which a closed-form solution exists for the corresponding European option. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Corporate bond spreads are affected by both credit risk and liquidity and it is difficult to disentangle the two factors empirically. In this paper we separate out the credit risk component by examining bonds that are issued by the same firm and that trade on the same day, allowing us to examine the effects of liquidity in a sample of bond pairs. We examine standard liquidity measures to determine how well they explain the differences in the two bonds’ yield spreads and find that the proxies do a poor job of measuring liquidity effects. Incorporating liquidity proxies related to other bonds issued by the firm and those for bonds of other firms can significantly improve the explanatory power. Still, a significant portion of the spread is left unexplained and it is largely driven by a common unknown factor. We conclude that good proxies for the liquidity component of corporate bond spreads remain elusive.

We examine the ability of structural models to predict credit spreads using global default data and security‐level credit spread data in eight developed economies. We find that two representative, pure default‐risk models tend to underpredict the average credit spreads on investment‐grade (IG) bonds, especially their spreads over government bonds, thereby providing evidence for a “global credit spread puzzle.” However, a model incorporating endogenous liquidity in the secondary debt market helps mitigate the puzzle. Furthermore, the model captures certain determinants of corporate bond market frictions across the eight economies and substantially improves the cross‐sectional fit of individual IG credit spreads.