This article investigates the effects of mortgage-backed security (MBS) hedging activity on interest rate volatility and proposes a model that takes these effects into account. An empirical examination suggests that the inclusion of information about MBSs considerably improves model performance in pricing interest rate options and in forecasting future interest rate volatility. The empirical results are consistent with the hypothesis that MBS hedging affects the interest rate volatility implied by both options and the actual interest rate volatility. The results also indicate that the inclusion of information about the MBS universe may result in models that better describe the price of fixed-income securities. The Author 2007. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

Dai and Singleton (2002) and Duffee (2002) show that there is a tension in affine term structure models between matching the mean and the volatility of interest rates. This article examines whether this tension can be solved by an alternative parametrization of the price of risk. The empirical evidence suggests that, first, the examined parametrization is not sufficient to solve the mean-volatility tension. Second, the usual result in the estimation of affine models, indicating that some of the state variables are extremely persistent, may have been caused by the lack of flexibility in the parametrization of the price of risk.

We show that a simple, intuitive variable, Goliath versus David (GVD), reflects time variation in discount rates related to changes in aggregate business conditions. GVD is the annual change in the weight of the largest 250 firms in the aggregate stock market and is motivated by research that shows that small firms are more severely impacted than large firms by economic shocks due to differences in access to external finance. We find that GVD is the best single predictor of out-of-sample market returns among traditional predictors, predicting quarterly market returns with an out-of-sample R 2 of 6.3% in the 1976–2011 evaluation period.

We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.

We conduct an analysis of the risk and return characteristics of a number of widely used 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.”

Although it is well known that appearance-based impressions affect labor market and election outcomes, little is known about the role appearance plays in financial transactions. We address this question using photographs of potential borrowers from a peer-to-peer lending site. Consistent with the trust-intensive nature of lending, we find that borrowers who appear more trustworthy have higher probabilities of having their loans funded. Moreover, borrowers who appear more trustworthy indeed have better credit scores and default less often. Overall, our findings suggest that impressions of trustworthiness matter in financial transactions as they predict investor, as well as borrower, behavior. A man I do not trust could not get money from me on all the bonds in Christendom. –John Pierpont Morgan, 1913

This paper considers the impact of Regulation Fair Disclosure (FD) on firms’ information environments and costs of capital. For NYSE/Amex firms we find little evidence of a change in the cost of capital attributable to Regulation FD. For Nasdaq firms we find that Regulation FD increased firms’ costs of capital by 10–19 basis points per annum though the statistical significance of this change is modest for some of our models. We also show substantial cross-sectional variation in the cost of capital changes. We find that cost of capital changes were negatively related to both pre-regulation firm size and PIN. In addition to the findings regarding Regulation FD, this research contributes to a growing literature that documents links between firms’ information environments and their costs of capital.

Numerous trading strategies examined in options research exhibit remarkably high mean returns and Sharpe ratios. We show some of these seemingly “good deals” are due to look-ahead biases. These biases stem from using information unavailable at the portfolio formation time to filter out observations suspected of being noisy or erroneous. Our results suggest that elevated Sharpe ratios may serve as potential indicators of such look-ahead biases. Furthermore, deviating from previous literature findings, we show that illiquidity is not strongly priced in stock options and that only a small set of stock characteristics are in fact associated with option expected returns.

The stylized fact that volatility is not priced in individual equity options does not withstand scrutiny. First, we show that the average return of heavily traded deep out‐of‐the‐money call options on stocks is −116 basis points per day. Second, Fama‐MacBeth estimates of the volatility risk premium in stock options are similar to those in S&P 500 Index call options. Third, the mean return of heavily traded delta‐hedged at‐the‐money calls (puts) is −23 (−30) basis points. Fourth, the variance risk premium in stock options is negative. Our analysis highlights the importance of microstructure biases and robustness in empirical work with options.