Mixed Exponential Power Asymmetric Conditional Heteroskedasticity

Mohammed Bouaddi and Jeroen Rombouts

Cahiers de recherche from CIRPEE

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

Keywords: Finite mixtures; exponential power distributions; conditional heteroskedasticity; asymmetry; heavy tails; value at risk (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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http://www.cirpee.org/fileadmin/documents/Cahiers_2007/CIRPEE07-49.pdf (application/pdf)

Related works:
Journal Article: Mixed Exponential Power Asymmetric Conditional Heteroskedasticity (2009)
Working Paper: Mixed exponential power asymmetric conditional heteroskedasticity (2007)
Working Paper: MIXED EXPONENTIAL POWER ASYMMETRIC CONDITIONAL HETEROSKEDASTICITY (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:lacicr:0749

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