This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast...

Existing methods of partitioning the market index into bull and bear regimes do not identify market corrections or bear market rallies. In contrast, our probabilistic model of the return distribution allows for rich and heterogeneous intra-regime dynamics. We focus on the characteristics and...

The COVID-19 pandemic has caused severe disruption to economic activity worldwide. This note analyzes what happened to the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. We identify bull and bear market regimes including their...

Existing methods of partitioning the market index into bull and bear regimes do not identify market corrections or bear market rallies. In contrast, our probabilistic model of the return distribution allows for rich and heterogeneous intra-regime dynamics. We focus on the characteristics and...

Measuring, modeling, and forecasting volatility are of great importance in financial applications such as asset pricing, portfolio management, and risk management. In this paper, we investigate predictability of stock market volatility by macrofinance variables in a dynamic regression framework...

This article uses a Markov-switching model that incorporates duration dependence to capture nonlinear structure in both the conditional mean and the conditional variance of stock returns. The model sorts returns into a high-return stable state and a low-return volatile state. We label these as...