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Volatility Clustering, Leverage Effects, and Jump Dynamics in the US and Emerging Asian Equity Markets

Author

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  • Daal, Elton

    (University of New Orleans)

  • Naka, Atsuyuki

    (University of New Orleans)

  • Yu, Jung-Suk

    (University of New Orleans)

Abstract

This paper proposes asymmetric GARCH-Jump models that synthesize autoregressive jump intensities and volatility feedback in the jump component. Our results indicate that these models provide a better fit for the dynamics of the equity returns in the US and emerging Asian markets, irrespective whether the volatility feedback is generated through a common GARCH multiplier or a separate measure of volatility in the jump intensity function. We also find that they can capture several distinguishing features of the return dynamics in emerging markets, such as, more volatility persistence, less leverage effects, fatter tails, and greater contribution and variability of the jump component.

Suggested Citation

  • Daal, Elton & Naka, Atsuyuki & Yu, Jung-Suk, 2006. "Volatility Clustering, Leverage Effects, and Jump Dynamics in the US and Emerging Asian Equity Markets," Working Papers 2005-03, University of New Orleans, Department of Economics and Finance.
  • Handle: RePEc:uno:wpaper:2005-03
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    References listed on IDEAS

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    1. Duan, Jin-Chuan, 1997. "Augmented GARCH (p,q) process and its diffusion limit," Journal of Econometrics, Elsevier, vol. 79(1), pages 97-127, July.
    2. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 33-55, March.
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    4. Harvey, Campbell R, 1995. "Predictable Risk and Returns in Emerging Markets," Review of Financial Studies, Society for Financial Studies, vol. 8(3), pages 773-816.
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    Citations

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    Cited by:

    1. Liu, Yuna, 2016. "Essays on Stock Market Integration - On Stock Market Efficiency, Price Jumps and Stock Market Correlations," Umeå Economic Studies 926, Umeå University, Department of Economics.
    2. Azimi, Mohammad Naim, 2015. "Modelling the Clustering Volatility of India's Wholesales Price Index and the Factors Affecting it," MPRA Paper 70267, University Library of Munich, Germany.
    3. Chang, Kuang-Liang, 2012. "The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model," Economic Modelling, Elsevier, vol. 29(6), pages 2298-2309.
    4. Liu, Yuna, 2016. "Stock exchange integration and price jump risks - The case of the OMX Nordic exchange mergers," Umeå Economic Studies 925, Umeå University, Department of Economics.
    5. Guo, Yanfeng & Wen, Xiaoqian & Wu, Yanrui & Guo, Xiumei, 2016. "How is China's coke price related with the world oil price? The role of extreme movements," Economic Modelling, Elsevier, vol. 58(C), pages 22-33.
    6. Cordis, Adriana S. & Kirby, Chris, 2014. "Discrete stochastic autoregressive volatility," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 160-178.
    7. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    8. Henry, Ólan T., 2009. "Regime switching in the relationship between equity returns and short-term interest rates in the UK," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 405-414, February.
    9. repec:rjr:romjef:v::y:2017:i:3:p:37-53 is not listed on IDEAS
    10. Lai, Jing-yi, 2012. "Shock-dependent conditional skewness in international aggregate stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 72-83.

    More about this item

    Keywords

    Volatility feedback; Time-varying jump intensity; Volatility clustering; Leverage effect; Leptokurtosis;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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