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Is Volatility Clustering of Asset Returns Asymmetric?

Author

Listed:
  • 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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    Cited by:

    1. Dinghai Xu, 2019. "A Study on Volatility Spurious Almost Integration Effect: A Threshold Realized GARCH Approach," Working Papers 1903, University of Waterloo, Department of Economics, revised Dec 2019.
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    3. Phong Nguyen & Wei-han Liu, 2017. "Time-Varying Linkage of Possible Safe Haven Assets: A Cross-Market and Cross-asset Analysis," International Review of Finance, International Review of Finance Ltd., vol. 17(1), pages 43-76, March.
    4. Katarzyna Czech & Michał Wielechowski & Pavel Kotyza & Irena Benešová & Adriana Laputková, 2020. "Shaking Stability: COVID-19 Impact on the Visegrad Group Countries’ Financial Markets," Sustainability, MDPI, Open Access Journal, vol. 12(15), pages 1-18, August.
    5. 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.
    6. repec:gam:jecnmx:v:4:y:2016:i:2:p:20:d:66662 is not listed on IDEAS
    7. Bingduo Yang & Zongwu Cai & Christian M. Hafner & Guannan Liu, 2019. "Time-Varying Mixture Copula Models with Copula Selection," Working Papers 2019-07-05, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    8. 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.
    9. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.
    10. Yang, Bingduo & Cai, Zongwu & Hafner, Christian M. & Liu, Guannan, 2018. "Trending Mixture Copula Models with Copula Selection," IRTG 1792 Discussion Papers 2018-057, Humboldt University Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    12. Chyi Lin Lee, 2017. "An examination of the risk-return relation in the Australian housing market," International Journal of Housing Markets and Analysis, Emerald Group Publishing, vol. 10(3), pages 431-449, June.
    13. Bingduo Yang & Zongwu Cai & Christian M. Hafner & Guannan Liu, 2018. "Trending Mixture Copula Models with Copula Selection," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201809, University of Kansas, Department of Economics, revised Sep 2018.
    14. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018, November.
    15. 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.
    16. 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.
    17. 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.
    18. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, Open Access Journal, vol. 11(12), pages 1-17, December.
    19. 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.
    20. repec:uts:finphd:38 is not listed on IDEAS
    21. Ana Carolina Costa Correa & Tabajara Pimenta Júnior & Luiz Eduardo Gaio, 2018. "Interdependence and asymmetries: Latin American ADRs and developed markets," Brazilian Business Review, Fucape Business School, vol. 15(4), pages 391-409, July.

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    More about this item

    Keywords

    Volatility clustering; Copulas; Realized volatility; High-frequency data.;
    All these keywords.

    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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