期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2007
卷号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：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 stationary. 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 stationary processes. Results on NASDAQ index
returns are similar.
关键词：finite mixtures, exponential power distributions, conditional heteroskedasticity,
asymmetry, heavy tails, value at risk.
