We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties. We investigate if this week's realized moments are informative for the cross-section of next week's stock returns. We...

State-of-the-art stochastic volatility models generate a 'volatility smirk' that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also adequately explain how the volatility smirk moves up and down in response to changes in risk....

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 strongly supported by the data. In the...

We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most...

This chapter surveys the methods available for extracting information from option prices that can be used in forecasting. We consider option-implied volatilities, skewness, kurtosis, and densities. More generally, we discuss how any forecasting object that is a twice differentiable function of...

Many studies have documented that daily realized volatility estimates based on intraday returns provide volatility forecasts that are superior to forecasts constructed from daily returns only. We investigate whether these forecasting improvements translate into economic value added. To do so we...

A substantial portion of the variation in the market variance risk premium can be explained by the conditional covariance between the market return and its variance, which we refer to as the leverage effect. This finding holds at different data frequencies and for various sample periods, and it...

We present a new discrete-time GARCH jump framework that allows for rich dynamics in higher moments by combining heteroskedastic processes with fat-tailed innovations in returns and volatility. We provide a tractable risk neutralization framework allowing for option valuation with separate...