This paper proposes a discrete-state stochastic volatility model with duration-dependent mixing. The latter is directed by a high-order Markov chain with a sparse transition matrix. As in the standard first-order Markov-switching (MS)model, this structure can capture turning points and shifts in...

This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to estimate ex post latent volatility imply that standard time series models of the...

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

We provide an approach to forecasting the long-run (unconditional distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts...

This paper models different components of the return distribution which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. This mixture captures occasional large changes in price, due to the impact...

This paper models different components of the return distribution which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. This mixture captures occasional large changes in price, due to the impact...

Many finance questions require a full characterization of the distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample....

We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size...

Material news events can be potentially important sources of jumps in stock returns. We collect 21 million news articles associated with more than 9,000 publicly-traded companies and use textual analyses to derive measures to summarize the news. We find that stock return jumps (including...

A potential important source of jumps in stock returns can be material news events. In this paper, we collect 21 million news articles associated with more than 9000 publicly-traded companies and use textual analysis to derive measures summarizing those news. We find that measures of news flow...