We use a sample of option prices, and the method of Bakshi, Kapadia and Madan (2003), to estimate the ex ante higher moments of the underlying individual securities ’ risk-neutral returns distribution. We find that individual securities ’ volatility, skewness and kurtosis are strongly related to subsequent returns. Specifically, we find a negative relation between volatility and returns in the cross-section. We also find a significant relation between skewness and returns, with more negatively (positively) skewed returns associated with subsequent higher (lower) returns, while kurtosis is positively related to subsequent returns. To analyze the extent to which these returns relations represent compensation for risk, we use data on index options and the underlying index to estimate the stochastic discount factor over the 1996-2005 sample period, and allow the stochastic discount factor to include higher moments. We find evidence that, even after controlling for differences in co-moments, individual securities ’ skewness matters. However, when we combine information in the risk-neutral distribution and the stochastic discount factor to estimate the implied physical distribution of industry returns, we find little evidence that the distribution of technology stocks was positively skewed during the bubble period–in fact, these stocks have the lowest skew, and the highest estimated Sharpe ratio, of all stocks in our sample. All errors are the responsibility of the authors. We thank Robert Battalio, Patrick Dennis, and Stewart Mayhew for providing data and computational code. We thank Andrew Ang, Leonce Bargeron, and Paul Pfleiderer

We provide a theoretical model linking firm characteristics and expected returns. The key ingredient of our model is technological shocks embodied in new capital (IST shocks), which affect the profitability of new investments. Firms ’ exposure to IST shocks is endogenously determined by the fraction of firm value due to growth opportunities. In our structural model, several firm characteristics – Tobin’s Q, past investment, earnings-price ratios, market betas, and idiosyncratic volatility of stock returns – help predict the share of growth opportunities in the firm’s market value, and are therefore correlated with the firm’s exposure to IST shocks and risk premia. Our calibrated model replicates: i) the predictability of returns by firm characteristics; ii) the comovement of stock returns on firms with similar characteristics; iii) the failure of the CAPM to price portfolio returns of firms sorted on characteristics; iv) the time-series predictability of market portfolio returns by aggregate investment and valuation ratios; and v) a downward sloping term structure of risk premia for dividend strips. Our model delivers testable predictions about the behavior of firm-level real variables – investment and output growth – that are supported by the data.

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We provide evidence that the positive relation between firm-level stock returns and firm-level return volatility is due to firms ’ real options. Consistent with real option theory, we find that the positive volatilityreturn relation is much stronger for firms with more real options and that the sensitivity of firm value to changes in volatility declines significantly after firms exercise their real options. We reconcile the evidence at the aggregate and firm levels by showing that the negative relation at the aggregate level may be due to aggregate market conditions that simultaneously affect both market returns and return volatility.

Individual investors often invest actively and lose thereby. Social interaction seems to exacerbate this tendency. In our model, senders' propensity to discuss their strategies' returns, and receivers' propensity to be converted, are increasing in sender return. The rate of conversion of investors to active investing is convex in sender return. Unconditionally, active strategies (high variance, skewness, and personal involvement) dominate the population unless the mean return penalty to active investing is too large. Thus, the model can explain overvaluation of 'active' asset characteristics even when investors have no inherent preference over them.

We find that firm-level variance risk premium, defined as the difference between model-free implied and expected variances, has the leading explaining power for firmlevel credit spreads, with the presence of market- and firm-level control variables identified in existing literature. Such a predictability complements the primary state variable—leverage ratio—in Merton-type framework and strengthens significantly when firm’s credit standing lowers to speculative grade. The strong forecastability of implied variance for credit spreads, emphasized by previous research, can be largely explained by variance risk premium. These findings point to a structural-form model with a stochastic variance risk being priced, potentially due to its exposure to macroeconomic uncertainty risk. JEL Classification: G12, G13, G14.

We assess the impact of the Sarbanes-Oxley Act of 2002 on corporate investment in an investment Euler equation framework, where a dummy for the passage of the Act is allowed to affect the rate at which managers discount future investment payoffs. Using generalized method of moments estimators, we find that the rate U.S. firm managers apply to discount investment projects rises significantly after 2002, while the discount rate for U.K. firms remains unchanged. The effects of the legislation on corporate investment are asymmetric, and are much more significant among relatively small firms. We also find that well-governed firms, firms with a credit rating, and accelerated filers of Section 404 of the Act have become more cautious about investment.

I investigate a pricing kernel in which coskewness and the market volatility risk factors are endogenously determined. I show that the price of coskewness and market volatility risk are restricted by investor risk aversion and skewness preference. Consistent with theory, I find that the pricing kernel is decreasing in the aggregate wealth and increasing in the market volatility. When I project my estimated pricing kernel on a polynomial function of the market return, doing so produces the puzzling behaviors observed in pricing kernel. Using pricing kernels, I examine the sources of the idiosyncratic volatility premium. I find that nonzero risk aversion and firms non-systematic coskewness determines the premium on idiosyncratic volatility risk. When I control for the non-systematic coskewness factor, I find no significant relation between idiosyncratic volatility and stock expected return. My results are robust across different sample periods, different measures of market volatility and firms characteristics.

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