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