Modeling Conditional Skewness in Stock Returns
AbstractIn this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal The European Journal of Finance.
Volume (Year): 13 (2007)
Issue (Month): 8 ()
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Other versions of this item:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
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- Christian Bauer, 2007. "A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH," The European Journal of Finance, Taylor & Francis Journals, vol. 13(1), pages 65-87.
- Lai, Jing-yi, 2012. "Shock-dependent conditional skewness in international aggregate stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 72-83.
- Matteo Grigoletto & Francesco Lisi, 2011. "Practical implications of higher moments in risk management," Statistical Methods and Applications, Springer, vol. 20(4), pages 487-506, November.
- Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tedongap, 2013. "Which Parametric Model for Conditional Skewness?," Working Papers 13-32, Bank of Canada.
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