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Mixed exponential power asymmetric conditional heteroskedasticity

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  • BOUADDI, Mohammed
  • ROMBOUTS, Jeroen V.K.

Abstract

To match the stylized facts of high frequency financial time series precisely andparsimoniously, this paper presents a finite mixture of conditional exponential powerdistributions where each component exhibits asymmetric conditional heteroskedasticity. Weprovide stationarity conditions and unconditional moments to the fourth order. We apply thisnew class to Dow Jones index returns. We find that a two-component mixed exponentialpower distribution dominates mixed normal distributions with more components, and moreparameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, allthe conditional variance processes become stationary. This happens because the mixedexponential power distribution allows for component-specific shape parameters so that it canbetter capture the tail behaviour. Therefore, the more general new class has attractive featuresover mixed normal distributions in our application: Less components are necessary and theconditional variances in the components are stationary processes. Results on NASDAQ indexreturns are similar.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2007097.

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Date of creation: 01 Dec 2007
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Handle: RePEc:cor:louvco:2007097

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Keywords: finite mixtures; exponential power distributions; conditional heteroskedasticity; asymmetry; heavy tails; value at risk;

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Citations

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Cited by:
  1. Jeroen V.K. Rombouts & Lars Stentoft, 2009. "Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models," Cahiers de recherche 0926, CIRPEE.
  2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, . "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
  3. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
  4. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Society for Computational Economics, vol. 38(4), pages 517-539, November.

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