Sequential Stock Return Prediction Through Copulas

Audrone Virbickaite, Christoph Frey () and Demian N. Macedo ()
Additional contact information
Christoph Frey: Erasmus School of Economics (ESE), Erasmus University Rotterdam, Postal: Burgemeester Oudlaan 50, 3062 PA Rotterdam., https://sites.google.com/site/christophfrey/
Demian N. Macedo: Universitat de les Illes Balears, Postal: Edifici Jovellanos, Crta Valldemossa, km 7,5 07122 Palma de Mallorca (Spain), https://sites.google.com/view/demianmacedo/home

No 91, DEA Working Papers from Universitat de les Illes Balears, Departament d'Economía Aplicada

Abstract: In this paper we perform density prediction for the equity returns in a non-linear manner by employing a copula-based approach. The use of asymmetric copulas enables us to model asymmetric predictive densities and non-linear dependencies between equity returns and some predictor variable. In our proposed approach, the copula parameter and the marginals are estimated simultaneously by using Sequential Monte Carlo techniques. We apply proposed models to daily log returns of 20 assets traded at the NYSE. We show that the realized volatility based models are preferred on average to the stochastic volatility based models. Moreover, asymmetric copula is preferred by more assets than the symmetric copula, advocating the use of non-linear models. Also, dividend yield is a better predictor variable than the lagged returns overall, but this result is reversed if we consider a volatile period only. Finally, hierarchical dependence parameter structure is preferred to dynamic or static approaches.

Keywords: Bayes Factor; Sequential Monte Carlo; Particle filters (search for similar items in EconPapers)
JEL-codes: C11 C53 C58 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:ubi:deawps:91

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