Bootstrap-Based Test for Volatility Shifts in GARCH against Long-Range Dependence

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Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.
Publisher
KOREAN STATISTICAL SOC
Issue Date
2015-09
Language
English
Article Type
Article
Citation

COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.22, no.5, pp.495 - 506

ISSN
2287-7843
DOI
10.5351/CSAM.2015.22.5.495
URI
http://hdl.handle.net/10203/285773
Appears in Collection
MA-Journal Papers(저널논문)
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