Tests for Volatility Shifts in Garch Against Long-Range Dependence

Taewook Lee, Moosup Kim and Changryong Baek

Journal of Time Series Analysis, 2015, vol. 36, issue 2, 127-153

Abstract: type="main" xml:id="jtsa12098-abs-0001"> Many empirical findings show that volatility in financial time series exhibits high persistence. Some researchers argue that such persistency is due to volatility shifts in the market, while others believe that this is a natural fluctuation explained by stationary long-range dependence models. These two approaches confuse many practitioners, and forecasts for future volatility are dramatically different depending on which models to use. In this article, therefore, we consider a statistical testing procedure to distinguish volatility shifts in generalized AR conditional heteroscedasticity (GARCH) model against long-range dependence. Our testing procedure is based on the residual-based cumulative sum test, which is designed to correct the size distortion observed for GARCH models. We examine the validity of our method by providing asymptotic distributions of test statistic. Also, Monte Carlo simulations study shows that our proposed method achieves a good size while providing a reasonable power against long-range dependence. It is also observed that our test is robust to the misspecified GARCH models.

Date: 2015
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