We estimate a continuous-time model with dynamic crash probability using the S&P500 index options and high-frequency information. We find that market illiquidity is an important factor in explaining the time-varying stock market crash risk embedded in index options. While market illiquidity and...

We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms. The total variance risk premium (VRP) represents the premium paid to insure against fluctuations in bad variance...

We estimate a continuous-time model with stochastic volatility and dynamic crash probability for the S&P 500 index and find that market illiquidity dominates other factors in explaining the stock market crash risk. While the crash probability is time-varying, its dynamic depends only weakly on...

We introduce the Homoscedastic Gamma [HG] model where the distribution of returns is characterized by its mean, variance and an independent skewness parameter under both measures. The model predicts that the spread between historical and risk-neutral volatilities is a function of the risk...

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows current asset returns to be asymmetric conditional on...

This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness models. This procedure is based on regressing the realized skewness measures on model-implied conditional skewness values. We find...

Cochrane and Piazzesi (2005) show that (i) lagged forward rates improve the predictability of annual bond returns, adding to current forward rates, and that (ii) a Markovian model for monthly forward rates cannot generate the pattern of predictability in annual returns. These results stand as a...

We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes that extends the class of existing autoregressive gamma (ARG) processes in one important dimension: each conditional moment dynamic is driven by a different and identifiable...

We document that the term structures of risk-neutral expected loss and gain uncertainty on the S&P500 returns are upward sloping on average. These shapes mainly reflect the higher premium required by investors to hedge downside risk, and the belief that potential gains will increase in the...

Expected returns vary when investors face time-varying investment opportunities. In theory, structural long-run risk models (Bansal and Yaron, 2004) and no-arbitrage affine models (Duffie, Pan, and Singleton, 2000) emphasize sources of risk that are not observable to the econometrician. We show...