Factor-based asset pricing models have been used to explain the common predictable variation in excess asset returns. This paper combines means with volatilities of returns in several futures markets to explain their common predictable variation. Using a latent variables methodology, tests do not reject a single factor model with a common time-varying factor loading. The single common factor accounts for up to 53% of the predictable variation in the volatilities and up to 14% of the predictable variation in the means. S&P500 futures volatility predicted by the factor model is highly correlated with volatility implied in S&P500 futures options. But both the factor and implied volatilities are significant in predicting future volatility. In derivatives pricing, both implied volatility from options and factors extracted from asset pricing models should be employed.

Previous studies document negative long-term abnormal stock returns following seasoned equity offering (SEO) issuances and conclude that markets are inefficient. Other studies, however, argue that these results are a manifestation of risk mismeasurement (i.e., the bad-model problem), not market inefficiency. We test the efficient market hypothesis (EMH) and avoid the bad-model problem by examining the long-term performance of our sample firms' bonds and stocks following their SEOs. Our results are inconsistent with the EMH. We also provide evidence that SEOs transfer wealth from shareholders to bondholders because SEOs reduce default risk.

Previous studies document negative long-term abnormal stock returns following seasoned equity offering (SEO) issuances and conclude that markets are inefficient. Other studies, however, argue that these results are a manifestation of risk mismeasurement (i.e., the bad-model problem), not market inefficiency. We test the efficient market hypothesis (EMH) and avoid the bad-model problem by examining the long-term performance of our sample firms' bonds and stocks following their SEOs. Our results are inconsistent with the EMH. We also provide evidence that SEOs transfer wealth from shareholders to bondholders because SEOs reduce default risk.

Many previous studies document a positive relation between research and development (R&D) and equity value. Though R&D can increase equity value by increasing firm value, it can also increase equity value at the expense of bondholder wealth through an increase in firm risk because equity is analogous to a call option on the underlying firm value. Shi [2003] tests this hypothesis by examining the relation between a firm's R&D intensity and its bond ratings and risk premiums at issuance. His results show that the net effect of R&D is negative for bondholders. We reexamine Shi's [2003] findings and in so doing make three contributions to the literature. First, we find that Shi's [2003] results are sensitive to the method of measuring R&D intensity. When we use what we argue is a better measure of R&D intensity, we find that the net effect of R&D is positive for bondholders. Second, when we use tests that Shi [2003] recognizes are even better than the ones that he uses, we find even stronger evidence of the positive effect of R&D on bondholders. Third, we examine cross‐sectional differences in the effect of R&D on debtholders. Consistent with our main finding, we document a negative relation between R&D increases and default risk. The default risk reduction is also more pronounced for firms with higher initial default scores (where the debtholders have more to gain from an R&D increase) and for firms with more bank debt (where the debtholders have greater covenant protection from the possible detriments associated with R&D increases).

We present a new methodology for estimating time-varying conditional skewness. Our model allows for changing means and variances, uses a maximum likelihood framework with instruments, and assumes a non-central t distribution. We apply this method to daily, weekly, and monthly stock returns, and find that conditional skewness is important. In particular, we show that the evidence of asymmetric variance is consistent with conditional skewness. Inclusion of conditional skewness also impacts the persistence in conditional variance.

We examine a sample of 8,313 cases, between 1951 and 2001, where firms unexpectedly increase their research and development (R&D) expenditures by a significant amount. We find consistent evidence of a misreaction, as manifested in the significantly positive abnormal stock returns that our sample firms' shareholders experience following these increases. We also find consistent evidence that our sample firms experience significantly positive long‐term abnormal operating performance following their R&D increases. Our findings suggest that R&D increases are beneficial investments, and that the market is slow to recognize the extent of this benefit (consistent with investor underreaction).

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross‐sectional variation of expected returns across assets and is significant even when factors based on size and book‐to‐market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.

The recent U.S. financial crisis and governmental bailout of financial institutions have intensified the debate on the need for effectively measuring and monitoring the financial institutions’ risks. This paper contributes to this discussion by introducing a market-based capital measurement that better captures the dynamics of bank risk and returns. Evidence confirms that these market-based capital adequacy metrics are much more sensitive to risk factors and more responsive to economic events than the traditional accounting/regulatory report based capital models, which often underestimate the true capital needs. The CDS premia, another market-bases solvency measure, seems to overreact to declines in capital adequacy.

Using account level data from a credit bureau, we study the role that social capital plays in consumer default decisions. We find that borrowers in communities with greater social capital are significantly less likely to default on loans, even after adjusting for different levels of income and other characteristics such as credit scores. The results are strongest for potentially strategic defaults on mortgages; a one standard deviation increase in social capital reduces such defaults by 12.4 %. These results can be generalized to any mortgage default. Our results also indicate that the effect of social capital is most prominent among more creditworthy borrowers, suggesting that when given a choice, the social cost of defaulting is an important factor affecting default decisions. We find a similar impact of social capital on consumer defaults in other datasets with more detailed information on borrowers as well. Our results are robust to modeling and methodology choices, as well as controlling for other drivers of default such as wealth, income and amenities from homeownership. Our results suggest that increasing social capital via measures to build community cohesion such as promotion of owner-occupied home ownership may be one avenue to deter consumer default.