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Is Volatility Clustering of Asset Returns Asymmetric?

Author

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  • Cathy Ning

    () (Department of Economics, Ryerson University, Toronto, Canada)

  • Dinghai Xu

    () (Department of Economics, University of Waterloo, Waterloo, Ontario, Canada)

  • Tony Wirjanto

    () (School of Accounting & Finance and Department of Statistics & Actuarial Science,University of Waterloo, Waterloo, Ontario, Canada)

Abstract

Volatility clustering is a well-known stylized feature of financial asset returns. In this paper, we investigate the asymmetric pattern of volatility clustering on both the stock and foreign exchange rate markets. To this end, we employ copula-based semi-parametric univariate time-series models that accommodate the clusters of both large and small volatilities in the analysis. Using daily realized volatilities of the individual company stocks, stock indices and foreign exchange rates constructed from high frequency data, we find that volatility clustering is strongly asymmetric in the sense that clusters of large volatilities tend to be much stronger than those of small volatilities. In addition, the asymmetric pattern of volatility clusters continues to be visible even when the clusters are allowed to be changing over time, and the volatility clusters themselves remain persistent even after forty days.

Suggested Citation

  • Cathy Ning & Dinghai Xu & Tony Wirjanto, 2014. "Is Volatility Clustering of Asset Returns Asymmetric?," Working Papers 050, Ryerson University, Department of Economics.
  • Handle: RePEc:rye:wpaper:wp050
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    1. repec:bla:irvfin:v:17:y:2017:i:1:p:43-76 is not listed on IDEAS
    2. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stober, 2016. "Regime switching vine copula models for global equity and volatility indices," Papers 1604.05598, arXiv.org.
    3. repec:gam:jecnmx:v:4:y:2016:i:2:p:20:d:66662 is not listed on IDEAS
    4. Aloui, Chaker & Hammoudeh, Shawkat & Hamida, Hela Ben, 2015. "Price discovery and regime shift behavior in the relationship between sharia stocks and sukuk: A two-state Markov switching analysis," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 121-135.
    5. repec:eee:finana:v:53:y:2017:i:c:p:94-111 is not listed on IDEAS
    6. Holger Fink & Yulia Klimova & Claudia Czado & Jakob Stöber, 2017. "Regime Switching Vine Copula Models for Global Equity and Volatility Indices," Econometrics, MDPI, Open Access Journal, vol. 5(1), pages 1-38, January.
    7. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    8. Ba Chu & Stephen Satchell, 2016. "Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence," Econometrics, MDPI, Open Access Journal, vol. 4(2), pages 1-21, March.
    9. Cao, Guangxi & Zhang, Minjia & Li, Qingchen, 2017. "Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 67-76.

    More about this item

    Keywords

    Volatility clustering; Copulas; Realized volatility; High-frequency data.;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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