Modeling Financial Return Dynamics via Decomposition

35 Pages Posted: 19 Sep 2008 Last revised: 12 Aug 2009

See all articles by Stanislav Anatolyev

Stanislav Anatolyev

New Economic School; CERGE-EI

Nikolay Gospodinov

Federal Reserve Bank of Atlanta


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.

Keywords: Stock returns predictability, Directional forecasting, Absolute returns, Joint predictive distribution, Copulas

JEL Classification: C22, C51, C53

Suggested Citation

Anatolyev, Stanislav and Gospodinov, Nikolay, Modeling Financial Return Dynamics via Decomposition. Journal of Business and Economic Statistics, Forthcoming, Available at SSRN:

Stanislav Anatolyev (Contact Author)

New Economic School ( email )

Skolkovskoe shosse, 45
Moscow, 121353

CERGE-EI ( email )

P.O. Box 882
7 Politickych veznu
Prague 1, 111 21
Czech Republic

Nikolay Gospodinov

Federal Reserve Bank of Atlanta ( email )

Atlanta, GA 30309
United States


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