Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes all assets are directed by a common discrete state...

The COVID-19 pandemic has caused severe disruption to economic and financial activity worldwide. We assess what happened to the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. Using the model of Maheu, McCurdy and Song (2012), we...

This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp,and estimate speculative bubbles. Three features make this new approach attractive to practitioners. First, the iHMM is capable of capturing the nonlinear dynamics of different types of bubble behaviors as it allows...

This paper develops an efficient approach to model and forecast time-series data with an unknown number of change-points. Using a conjugate prior and conditional on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. The...

This paper studies the relationship between commodity markets in two key regions of the international economy during the 1469-1914 period: the Ottoman Empire and Europe. By providing evidence on what thus far has been largely a qualitative discussion, we propose the first comprehensive empirical...

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 thesis develops new hidden Markov models and applies them to financial marketand macroeconomic time series.Chapter 1 proposes a probabilistic model of the return distribution with rich andheterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market...

This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Due to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is...

With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that incorporates unobserved heterogeneity. The proposed model...