We use Markov chain methods to develop a flexible class of discrete stochastic autoregressive volatility (DSARV) models. Our approach to formulating the models is straightforward, and readily accommodates features such as volatility asymmetry and time-varying volatility persistence. Moreover, it...

We develop regime-switching factor models in which the number of factors determines the operative economic regime. To illustrate the proposed methodology, we analyze the covariance structure of a widely-studied set of 25 equity portfolios

We develop regime-switching factor models in which the number of factors determines the operative economic regime. To illustrate the proposed methodology, we analyze the covariance structure of a widely-studied set of 25 equity portfolios

We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors is potentially very large, we impose a prior that...