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

KALAYLIOĞLU AKYILDIZ Z. I., Bozdemir B., Ghosh S. K.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, vol.42, no.6, pp.659-669, 2013 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 6
  • Publication Date: 2013
  • Journal Name: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.659-669
  • Middle East Technical University Affiliated: Yes

Abstract

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