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Modeling Financial Return Dynamics by Decomposition

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Author Info
Stanislav Anatolyev () (New Economic School)
Nikolay Gospodinov () (Concordia University)

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Abstract

While the predictability of excess stock returns is statistically small, their sign and volatility exhibit a substantially larger degree of dependence over time. We capitalize on this observation and consider prediction of excess stock returns by decomposing the equity premium into a product of sign and absolute value components and carefully modeling the marginal predictive densities of the two parts. Then we construct the joint density of a positively valued (absolute returns) random variable and a discrete binary (sign) random variable by copula methods and discuss computation of the conditional mean predictor. Our empirical analysis of US stock return data shows among other interesting ndings that despite the large unconditional correlation between the two multiplicative components they are conditionally very weakly dependent.

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Publisher Info
Paper provided by Center for Economic and Financial Research (CEFIR) in its series Working Papers with number w0095.

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Length: 29 pages
Date of creation: Jan 2007
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Handle: RePEc:cfr:cefirw:w0095

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Related research
Keywords: Stock returns predictability; Directional forecasting; Absolute returns; Joint predictive distribution; Copulas.;

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Dimitrios Thomakos & Tao Wang, 2007. "'Optimal' Probabilistic Predictions for Financial Returns," Working Papers 0006, University of Peloponnese, Department of Economics. [Downloadable!]
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