This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I introduce an affine jump-diffusion model, which accounts for both the factor structure of asset returns and that of the variance of idiosyncratic returns. The estimation is...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

We disentangle the risk of time-varying volatility and return in a consumption-based asset pricing model by introducing stochastic volatility of consumption growth to asset prices moving in volatility units instead of moving in time. This time-change approach yields additional insights to risk...

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage...

We seek fundamental risks from news text. Conceptually, news is closely related to the idea of systematic risk, in particular the "state variables" in the ICAPM. News captures investors' concerns about future investment opportunities, and hence drives the current pricing kernel. This paper...

Stockholders are faced with both macroeconomic uncertainty and uncertainty that is generated from fears. We develop a financial stress factor as a proxy for pessimism that operates through stockholders' expectations about the elevated market volatility and shocks the cross-section of stock...

This paper presents an empirical approach that combines competing paradigms of modeling in empirical capital market research. The approach simultaneously estimates the explanatory power of fundamentals, expectations, and historic yield patterns, making it possible to test the extent to which the...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

We provide a measure of sparsity for expected returns within the context of classical factor models. Our measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions...

This paper develops a rare disaster asset pricing model with EZ preferences, in particular including the impact of macroeconomic consequences of the COVID-19 disaster. I estimate the probability of disaster, disaster states, and the duration of disaster to shed light on the frequency and size of...