We investigate alternative unconditional and conditional distributional models for the returns on Japan's Nikkei 225 stock market index. Among them is the recently introduced class of ARMA-GARCH models driven by α-stable (or stable Paretian) distributed innovations, designed to capture the...

Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulated moments estimator for option volatilities described in Mizrach (2002); the second is a new approach...

We present a multivariate generalization of the mixed normal GARCH model proposed in Haas, Mittnik, and Paolella (2004a). Issues of parametrization and estimation are discussed. We derive conditions for covariance stationarity and the existence of the fourth moment, and provide expressions for...

While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many...

We develop a multivariate generalization of the Markov-switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth-moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a...

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic...

The use of Markov-switching models to capture the volatility dynamics of financial time series has grown considerably during past years, in part because they give rise to a plausible interpretation of nonlinearities. Nevertheless, GARCH-type models remain ubiquitous in order to allow for...

Both unconditional mixed normal distributions and GARCH models with fat-tailed conditional distributions have been employed in the literature for modeling financial data. We consider a mixed normal distribution coupled with a GARCH-type structure (termed MN-GARCH) which allows for conditional...

We develop a multivariate generalization of the Markov-switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth-moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a...