The purpose of this paper is to explore whether stock index volatility series exhibit real long memory.
The authors employ sequential procedure to test structural break in volatility series, and use DFA and 2ELW to estimate long memory parameter for the whole samples and subsamples, and further apply adaptive FIGARCH (AFIGARCH) to describe long memory and structural break.
The empirical results show that stock index volatility series are characterized by long memory and structural break, and therefore it is appropriate to use AFIGARCH to model stock index volatility process.
This study empirically investigates the properties of long memory and structural break in stock index volatility series. The conclusion has a certain reference value for understanding the properties of long memory and structural break in volatility series for academic researchers, market participants and policy makers, and for modeling and forecasting future volatility, testing market efficiency, pricing financial assets, constructing quantitative investment strategy and measuring market risk.
The authors acknowledge the research grants from National Social Science Foundation of China (No. 15BTJ032).
Luo, Y. and Huang, Y. (2019), "Long memory or structural break? Empirical evidences from index volatility in stock market", China Finance Review International, Vol. 9 No. 3, pp. 324-337. https://doi.org/10.1108/CFRI-11-2017-0222
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