[Observable covariates are useful for predicting default, but several studies question their value for explaining credit spreads. We introduce a discrete-time no-arbitrage model with observable covariates, which allows for a closed-form solution for the value of credit default swaps (CDS). The default intensity is a quadratic function of the covariates, specified such that it is always positive. The model yields economically plausible results in terms of fit, the economic impact of the covariates, and the prices of risk. Risk premiums are large and account for a smaller percentage of spreads for firms with lower credit quality. Macroeconomic and firm-specific information can explain most of the variation in CDS spreads over time and across firms, even with a parsimonious specification. These findings resolve the existing disconnect in the literature regarding the value of observable covariates for credit risk pricing and default prediction.]

Rather than assuming a fixed recovery rate in estimation, we estimate recovery rates from credit default swap spreads, using 3 years of daily data on 152 corporations. We use a quadratic pricing model, which ensures nonnegative default probabilities and recovery rates. The estimated cross section of recovery rates is plausible, with an average recovery rate of 54% and substantial cross-sectional variation. Estimated 5-year default probabilities are on average 67% higher than default probabilities obtained using the standard 40% recovery assumption. This finding critically impacts the valuation of structured credit products. Larger firms and firms with more tangible assets have higher recovery rates.

We nest multiple volatility components, fat tails, and a U-shaped pricing kernel in a single option model and compare their contribution in describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most important and improves option fit by 17%, on average, and more so for two-factor models. A second volatility component improves the option fit by 9%, on average. Fat tails improve option fit by just over 4%, on average, but more so when a U-shaped pricing kernel is applied. Overall, these three model features are complements rather than substitutes: the importance of one feature increases in conjunction with the others.

Equity risk measured by beta is of great interest to both academics and practitioners. Existing estimates of beta use historical returns. Many studies have found option-implied volatility to be a strong predictor of future realized volatility. We find that option-implied volatility and skewness are also good predictors of future realized beta. Motivated by this finding, we establish a set of assumptions needed to construct a beta estimate from option-implied return moments using equity and index options. This beta can be computed using only option data on a single day. It is therefore potentially able to reflect sudden changes in the structure of the underlying company.

This paper investigates the importance of idiosyncratic consumption risk for the cross‐sectional variation in asset returns. We find that besides the rate of aggregate consumption growth, the cross‐sectional variance of consumption growth is also a priced factor. This suggests that consumers are not fully insured against idiosyncratic consumption risk, and that asset returns reflect their attempts to reduce their exposure to this risk. The resulting two‐factor consumption‐based asset pricing model significantly outperforms the CAPM, and its performance compares favorably with that of the Fama–French three‐factor model.

Both large oil price increases and decreases are associated with deteriorating economic conditions. The projection of the state price density (SPD) onto oil returns estimated from oil futures and option prices displays a U-shaped pattern. Because investors assign high state prices to large negative and large positive oil returns, the U-shaped SPD may steepen in either tail when economic conditions deteriorate. The positive return region of the SPD is more closely related to economic conditions. The oil SPD contains information about economic conditions and future security returns that is distinct from the information in the stock index SPD.

We specify and estimate a no-arbitrage model for sovereign CDS contracts in which countries’ default intensities depend on economic and financial indicators. To facilitate identification and to distinguish the importance of local and global covariates, we estimate a model with three global and four local covariates using CDS spreads for five maturities and twenty-five countries. The model provides a good fit. The impact of the economic and financial variables on spreads is consistent with economic intuition, and substantially varies across countries and over time. Estimated risk premiums are highly variable and peak during the 2008 financial crisis for most countries.

We analyze the relation between expected option returns and the volatility of the underlying securities. The expected return from holding a call (put) option is a decreasing (increasing) function of the volatility of the underlying. These predictions are supported by the data. In the cross section of equity option returns, returns on call (put) option portfolios decrease (increase) with underlying stock volatility. This finding is not due to cross-sectional variation in expected stock returns. It holds in various option samples with different maturities and moneyness, and is robust to alternative measures of underlying volatility and different weighting methods.

We develop a tractable dynamic model of an index option market maker with limited capital. We solve for the variance risk premium and option prices as a function of the asset dynamics and market maker option holdings and wealth. The market maker absorbs end users’ positive demand and requires a more negative variance risk premium when she incurs losses. We estimate the model using returns, options, and inventory and find that it performs well, especially during the financial crisis. The restrictions imposed by nested existing reduced-form stochastic-volatility models are strongly rejected in favor of the model with a market maker.

The conditional covariance between the market return and its variance, which we refer to as the leverage effect, is positively related to the variance risk premium. It contains incremental information about the variance risk premium after controlling for other return moments and additional variables suggested by the literature as determinants of the variance risk premium. This empirical finding is supported by theory: the pricing of volatility risk is the economic channel behind the strong positive relation between the two variables. We use this relation to construct a time series of the variance risk premium dating back to 1926. (JEL G12, G13) Received February 7, 2020; editorial decision September 01, 2021.