Modeling the time-varying skewness via decomposition for out-of-sample forecast
This paper models time-varying skewness for financial return dynamics. We decompose nancial returns into the product of the absolute returns and signs, so-called the intriguing decomposition. The joint distribution between the decomposed components is modeled through a copula function with marginals. Allowing the copula dependence parameter time-varying, we estimate the dynamic nonlinear dependence between absolute returns and signs, which governs time- varying skewness for out-of-sample forecast of financial returns. The empirical results in this paper show that the proposed models with dynamic dependence obtain better gains of out-of-sample fore- cast, and suggest the robust strategy for a risk-averse investor in response to the market timing. This paper also explores the sources of the forecasting performance via a recently developed econometric pin-down approach. Beyond the pure statistical sense, we find that the forecasts of time-varying skewness trace closely to NBER-dated business-cycle phases.
|Date of creation:||30 Aug 2011|
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Web page: https://mpra.ub.uni-muenchen.de
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- Ole E. Barndorff-Nielsen & Neil Shephard, 2006.
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- Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Multiplicative Error Models," Econometrics Working Papers Archive 2011_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Apr 2011. Full references (including those not matched with items on IDEAS)
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