This paper estimates a switching autoregressive conditional heteroskedastic time series model for returns on the daily German stock market index. Volatility clustering is captured by persistent periods of different volatility levels and by the dependence on past innovations. We introduce a...

We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast...

This paper estimates a switching autoregressive conditional heteroskedastic time series model for returns on the daily German stock market index. Volatility clustering is captured by persistent periods of different volatility levels and by the dependence on past innovations. We introduce a...

This paper estimates a switching autoregressive conditional heteroskedastic time series model for returns on the daily German stock market index. Volatility clustering is captured by persistent periods of different volatility levels and by the dependence on past innovations. We introduce a...

Using a panel of Austrian bank data we show that the lending decisions of the smallest banks are more sensitive to interest rate changes, and that for all banks, sensitivity changes over time. We propose to estimate the groups of banks that display similar lending reactions by means of a group...