In this paper we consider the theoretical and empirical relevance of a new family of conditionally heteroskedastic models with a trend dependent conditional variance equation: the Trend-GARCH model. The interest in these models lies in the fact that modern microeco- nomic theory often suggests the connection between the past behavior of time series and the subsequent reaction of market individuals and thereon changes in the future characteristics of the time series. Our results reveal important properties of these models, which are con- sistent with stylized facts in financial data sets. They can also be employed for model identification, estimation, and testing. The em- pirical analysis of a broad variety of asset prices significantly supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, or TGARCH in replicating the leverage effect in the conditional variance and in fitting the news impact curve.
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Article provided by Department of Economics, Economics I, Bayreuth University in its journal European Journal of Finance.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990.
"Noise Trader Risk in Financial Markets,"
Journal of Political Economy,
University of Chicago Press, vol. 98(4), pages 703-38, August.
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