Modeling Financial Return Dynamics via Decomposition
将股票超额收益分解为符号和绝对值两部分，分别建模后结合copula，捕捉传统回归无法体现的非线性依赖，提升方向与波动预测能力。
Abstract While the predictability of excess stock returns is detected by traditional predictive regressions as statistically small, the direction-of-change and volatility of returns exhibit a substantially larger degree of dependence over time. We capitalize on this observation and decompose the returns into a product of sign and absolute value components whose joint distribution is obtained by combining a multiplicative error model for absolute values, a dynamic binary choice model for signs, and a copula for their interaction. Our decomposition model is able to incorporate important nonlinearities in excess return dynamics that cannot be captured in the standard predictive regression setup. The empirical analysis of U.S. stock return data shows statistically and economically significant forecasting gains of the decomposition model over the conventional predictive regression. Keywords: : Absolute returnsCopulasDirectional forecastingJoint predictive distributionStock returns predictability