Optimal Portfolio Choice under Decision-Based Model Combinations

30 Pages Posted: 1 Nov 2014 Last revised: 5 Nov 2015

See all articles by Davide Pettenuzzo

Davide Pettenuzzo

Brandeis University - International Business School

Francesco Ravazzolo

Free University of Bozen-Bolzano - Faculty of Economics and Management; BI Norwegian Business School

Date Written: November 4, 2015

Abstract

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility-based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time-varying coefficients, stochastic volatility models. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out-of-sample predictability.

Keywords: Bayesian econometrics, Time-varying parameters, Model combinations, Portfolio choice

JEL Classification: C11, C22, G11, G12

Suggested Citation

Pettenuzzo, Davide and Ravazzolo, Francesco, Optimal Portfolio Choice under Decision-Based Model Combinations (November 4, 2015). Available at SSRN: https://ssrn.com/abstract=2516950 or http://dx.doi.org/10.2139/ssrn.2516950

Davide Pettenuzzo (Contact Author)

Brandeis University - International Business School ( email )

Mailstop 32
Waltham, MA 02454-9110
United States

Francesco Ravazzolo

Free University of Bozen-Bolzano - Faculty of Economics and Management ( email )

Via Sernesi 1
39100 Bozen-Bolzano (BZ), Bozen 39100
Italy

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

HOME PAGE: http://www.francescoravazzolo.com/

Register to save articles to
your library

Register

Paper statistics

Downloads
157
Abstract Views
938
rank
192,253
PlumX Metrics