References (50)


Citations (2)



Modeling Financial Return Dynamics via Decomposition

Stanislav Anatolyev

New Economic School; CERGE-EI

Nikolay Gospodinov

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

Open PDF in Browser Download This Paper

Date posted: September 19, 2008 ; Last revised: August 12, 2009

Suggested Citation

Anatolyev, Stanislav and Gospodinov, Nikolay, Modeling Financial Return Dynamics via Decomposition. Journal of Business and Economic Statistics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=982827

Contact Information

Stanislav A. Anatolyev (Contact Author)
New Economic School ( email )
47 Nakhimovsky Prospekt
Moscow, 117418
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
HOME PAGE: https://www.frbatlanta.org/research/economists/gospodinov-nikolay.aspx?panel=1
Feedback to SSRN

Paper statistics
Abstract Views: 3,330
Downloads: 170
Download Rank: 139,658
References:  50
Citations:  2