ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH

Kurt Brännäs () and Jan G. De Gooijer ()

No 535, Umeå Economic Studies from Umeå University, Department of Economics

Abstract: The asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of Sentana (1995). We introduce a framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. Some of the new model's moment properties are also derived. Empirical results are given for the daily returns of the composite index of the New York Stock Exchange. There is strong evidence of asymmetry in both the conditional mean and conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best fitted asMA-asQGARCH model is compared to pure asMA and no-change forecasts. This is done both in terms of conditional mean forecasting as well in terms of risk forecasting.

Keywords: Time series; finance; nonlinearity; estimation; testing; forecasting; NYSE (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 C53 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin and nep-fmk
Date: 2000-05-16
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Published in Journal of Forecasting , 2004, pages 155-171.

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Related works:
Working Paper: Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH (2000)
Journal Article: Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH (2004)
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Persistent link: http://EconPapers.repec.org/RePEc:hhs:umnees:0535

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