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Asymmetries in conditional mean and variance: Modelling stock returns by asMA-asQGARCH
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
2004 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 23, no 3, 155-171 p.Article in journal (Refereed) Published
Abstract [en]

We propose a nonlinear time series model where both the conditional mean and the conditional variance are asymmetric functions of past information. The model is particularly useful for analysing financial time series where it has been noted that there is an asymmetric impact of good news and bad news on volatility (risk) transmission. We introduce a coherent framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. To this end we derive both a Wald and a Lagrange multiplier test. Some of the new asymmetric model's moment properties are investigated. Detailed 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 the conditional variance functions. In a genuine out-of-sample forecasting experiment the performance of the best fitted asymmetric model, having asymmetries in both conditional mean and conditional variance, is compared with an asymmetric model for the conditional mean, and with no-change forecasts. This is done both in terms of conditional mean forecasting as well as in terms of risk forecasting. Finally, the paper presents some evidence of asymmetries in the index stock returns of the Group of Seven (G7) industrialized countries.

Place, publisher, year, edition, pages
2004. Vol. 23, no 3, 155-171 p.
Keyword [en]
Estimation, Finance, Forecasting, Nonlinearity, NYSE, Testing, Time series
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Research subject
URN: urn:nbn:se:umu:diva-64755DOI: 10.1002/for.910OAI: diva2:602705
Available from: 2013-02-02 Created: 2013-02-02 Last updated: 2013-02-12Bibliographically approved

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