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 decompose the variance risk premium into upside and downside variance risk premia. These components reflect market compensation for changes in good and bad uncertainties. Their difference is a measure of the skewness risk premium (SRP), which captures asymmetric views on favorable versus...

We greatly expand the space of tractable term-structure models. We consider one example that combines positive yields with rich volatility and correlation dynamics. Bond prices are expressed in closed form and estimation is straightforward. We find that the early stages of a recession have...

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...

Specifications of the Federal Reserve target rate that have more realistic features mitigate in-sample over-fitting and are favored in the data. Imposing a positivity constraint and discrete increments significantly increases the accuracy of model out-of-sample forecasts for the level 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 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...