This paper introduces a novel Bayesian time series model that combines the nonparametric features of an infinite hidden Markov model with the volatility persistence captured by the GARCH framework, to effectively model and forecast short-term interest rates. When applied to US 3-month Treasury...

This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood based estimation. Parametric and nonparametric versions are introduced. Due to the computational advantages of our approach we can model the factor nonparametrically as a...

This paper shows that oil shocks primarily impact economic growth through the conditional variance of growth. We move beyond the literature that focuses on conditional mean point forecasts and compare models based on density forecasts. Over a range of dynamic models, oil shock measures and data...

This paper shows that oil shocks primarily impact economic growth through the conditional variance of growth. We move beyond the literature that focuses on conditional mean point forecasts and compare models based on density forecasts. Over a range of dynamic models, oil shock measures and data...

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density...

The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian...