Modeling Financial Return Dynamics via Decomposition
New Economic School; CERGE-EI
Federal Reserve Bank of Atlanta
Journal of Business and Economic Statistics, Forthcoming
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 US stock return data shows statistically and economically significant forecasting gains of the decomposition model over the conventional predictive regression.
Number of Pages in PDF File: 35
Keywords: Stock returns predictability, Directional forecasting, Absolute returns, Joint predictive distribution, Copulas
JEL Classification: C22, C51, C53
Date posted: September 19, 2008 ; Last revised: August 12, 2009
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