This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov...

The weekly change of several German milkbased commodity prices do not only exhibit traditional patterns like mean dependence and volatility clustering, but also a high frequency of zero changes which can not be explained by well known ARIMA-GARCH models. Therefore, we develop a new mixture model...

Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood. This...