REGIME SWITCHING STOCHASTIC VOLATILITY AND ITS EMPIRICAL ANALYSIS
Master of Science
Date of Defense:
John C Liechty, Thesis Advisor/Co-Advisor John C Liechty, Thesis Advisor/Co-Advisor
Stochastic Volatility Regime Switching MCMC
We consider the stochastic volatility model with regime switching. We model the unobserved regimes
as a continuous-time latent or hidden Markov chain, with exponential waiting times. This allows us to identify the hidden regimes and compare the posterior probability of changes in regimes with news events. Our model is very flexible: the number of possible regimes, the mean reverting, persistence and variance parameters in the volatility equation can all be controlled by different hidden Markov chains. We use Monte Carlo Markov Chain (MCMC) method for inference; model selection is based on calculating the posterior probability of the model, given the data via a reversible jump approach. We also consider maximum likelihood, and out of sample predictive validation. We report the analysis of several empirical dataset: S&P 500, Federal rate, exchange rate, and demonstrate proposed models out perform benchmark models with regards to fit and predictive ability.