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Mixed exponential power asymmetric conditional heteroskedasticity

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Author Info
BOUADDI, Mohammed
ROMBOUTS, Jeroen V.K.

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

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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