Modeling the time-varying skewness via decomposition for out-of-sample forecast
AbstractThis 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 41248.
Date of creation: 30 Aug 2011
Date of revision:
Time-varying skewness; Dynamic nonlinear dependence; Copulas; Out-of-sample forecast; Sources of forecasting performance;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G00 - Financial Economics - - General - - - General
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