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Modelling Good and Bad Volatility

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  • Pelagatti Matteo M

    (University of Milan - Bicocca)

Abstract

The returns of many financial assets show significant skewness, but in the literature this issue is only marginally dealt with. Our conjecture is that this distributional asymmetry may be due to two different dynamics in positive and negative returns.In this paper we propose a process that allows the simultaneous modelling of skewed conditional returns and different dynamics in their conditional second moments. The main stochastic properties of the model are analyzed and necessary and sufficient conditions for weak and strict stationarity are derived.An application to the daily returns on the principal index of the London Stock Exchange supports our model when compared to other frequently used GARCH-type models, which are nested into ours.

Suggested Citation

  • Pelagatti Matteo M, 2009. "Modelling Good and Bad Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-20, March.
    • Handle: RePEc:bpj:sndecm:v:13:y:2009:i:1:n:2
    DOI: 10.2202/1558-3708.1595
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    References listed on IDEAS

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    1. Richard Harris & C. Coskun Kucukozmen & Fatih Yilmaz, 2004. "Skewness in the conditional distribution of daily equity returns," Applied Financial Economics, Taylor & Francis Journals, vol. 14(3), pages 195-202.
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    7. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(4), pages 465-487, December.
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    9. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
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    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Tseng, Jie-Jun & Li, Sai-Ping, 2011. "Asset returns and volatility clustering in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1300-1314.
    3. Geon Ho Choe & Kyungsub Lee, 2013. "Conditional correlation in asset return and GARCH intensity model," Papers 1311.4977, arXiv.org.
    4. Geon Choe & Kyungsub Lee, 2014. "Conditional correlation in asset return and GARCH intensity model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(3), pages 197-224, July.
    5. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
    6. Tseng, Jie-Jun & Li, Sai-Ping, 2012. "Quantifying volatility clustering in financial time series," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 11-19.

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