Asymmetries and Volatility Regimes in the European Equity Markets
AbstractThis paper provides and empirical examination of four European equity indices between 1991 and 2005. We investigate the ability of fifteen different GARCH models to capture the characteristics of historical daily returns effectively and generate realistic implied volatility skews. Using many different model selection criteria we conclude that a normal mixture GARCH model with two volatility components, two sources of asymmetry and endogenous time-varying conditional higher moments provides the best fit overall. Since this model is relatively new in the literature we discuss the theoretical and empirical properties of such models. Examining the estimated parameters we show that they provide information on the likelihood of a crash and they specify the return and volatility behaviour, the leverage effect and the persistence of volatility during the two regimes (‘normal’ and ‘crash’). We also find that asymmetric normal mixture GARCH models, even without a volatility risk premium, afford a sufficiently rich structure to match the empirical characteristics of implied volatility skew surfaces, whereas single-state GARCH models give unrealistic shapes for the equity index skew.
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Bibliographic InfoPaper provided by Henley Business School, Reading University in its series ICMA Centre Discussion Papers in Finance with number icma-dp2005-14.
Length: 29 pages
Date of creation: Nov 2005
Date of revision:
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equity skew; market cras; GARCH process; normal mixture; skey peristence; leverage effect; volatility regimes;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
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- Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009.
"Asymmetric multivariate normal mixture GARCH,"
Computational Statistics & Data Analysis,
Elsevier, vol. 53(6), pages 2129-2154, April.
- Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
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