BAYESIAN UNIT-ROOT TESTING IN STOCHASTIC VOLATILITY MODELS WITH CORRELATED ERRORS

Abstract

A series of returns are often modeled using stochastic volatility models.Many observed financial series exhibit unit-root non-stationarybehavior in the latent AR(1) volatility process and tests for a unit-rootbecome necessary, especially when the error process of the returns iscorrelated with the error terms of the AR(1) process. In this paper, wedevelop a class of priors that assigns positive prior probability on thenon-stationary region, employ credible interval for the test, and showthat Markov Chain Monte Carlo methods can be implemented usingstandard software. Several practical scenarios and real examples areexplored to investigate the performance of our method.