ASYMMETRIES IN CONDITIONAL MEAN AND VARIANCE: MODELLING STOCK RETURNS BY asMA-asQGARCH
AbstractThe 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.
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Bibliographic InfoPaper provided by Umeå University, Department of Economics in its series Umeå Economic Studies with number 535.
Length: 21 pages
Date of creation: 16 May 2000
Date of revision:
Publication status: Published in Journal of Forecasting , 2004, pages 155-171.
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Postal: Department of Economics, Umeå University, S-901 87 Umeå, Sweden
Phone: 090 - 786 61 42
Fax: 090 - 77 23 02
Web page: http://www.econ.umu.se/
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Time series; finance; nonlinearity; estimation; testing; forecasting; NYSE;
Other versions of this item:
- Jan G. De Gooijer & Kurt Brännäs, 2004. "Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 155-171.
- Kurt Brännäs & Jan G. de Gooijer, 2000. "Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH," Tinbergen Institute Discussion Papers 00-049/4, Tinbergen Institute.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-05-22 (All new papers)
- NEP-ECM-2000-05-22 (Econometrics)
- NEP-ETS-2000-05-22 (Econometric Time Series)
- NEP-FIN-2000-05-22 (Finance)
- NEP-FMK-2000-05-22 (Financial Markets)
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