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...

A Gaussian copula regression model gives a tractable way of handling a multivariate regression when some of the marginal distributions are non-Gaussian. Our paper presents a general Bayesian approach for estimating a Gaussian copula model that can handle any combination of discrete and...

Growing interest in the measurement of subjective well-being (SWB) has also been accompanied by scientific debate on the optimal method for measuring SWB. The momentary perspective, which is represented by the ecological momentary assessment (EMA) and day reconstruction method (DRM), emphasizes...

DeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and...

A number of studies investigate whether various stochastic variables explain changes in return volatility by specifying the variables as covariates in a GARCH(1, 1) or EGARCH(1, 1) model. The authors show that these models impose an implicit constraint that can obscure the true role of the...

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 use state-space methods to investigate the relation between volume, volatility, and ARCH effects within a mixture of distributions hypothesis (MDH) framework. Most recent studies of the MDH fit AR(1) specifications that require the information flow to be highly persistent. Using a more...