We advocate the use of absolute moment ratio statistics in conjunctionwith standard variance ratio statistics in order to disentangle lineardependence, non-linear dependence, and leptokurtosis in financial timeseries. Both statistics are computed for multiple return horizonssimultaneously, and...

This article investigates the heteroscedastic behaviour of the Indian stock market using different GARCH models. First, the standard GARCH approach is used to investigate whether stock return volatility changes over time and if so, whether it is predictable. Then, the EGARCH models are applied...

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of...

This paper explores a possible cause of persistence in stock return volatility. Artificial stock markets are examined with different learning mechanisms, i.e. imitative and experiential learning. The simulation result shows that an economy with imitative learning gives rise to persistence of...

Following Alfarano et al. [Estimation of agent-based models: the case of an asymmetric herding model, Comput. Econ. 26 (2005) 19–49; Excess volatility and herding in an artificial financial market: analytical approach and estimation, in: W. Franz, H. Ramser, M. Stadler (Eds.),...

This paper proposes asymmetric GARCH-Jump models that synthesize autoregressive jump intensities and volatility feedback in the jump component. Our results indicate that these models provide a better fit for the dynamics of the equity returns in the US and emerging Asian markets, irrespective...

The paper describes simultaneous tests of the effects of announcements of UK mergers and acquisitions on both the mean and conditional volatility functions for UK bidder firms. Unlike previous research, the entire data set is utilized, thus avoiding researcher-chosen event periods. The...