Combining Multivariate Volatility Forecasts: An Economic-Based Approach

Journal of Financial Econometrics, Forthcoming

45 Pages Posted: 23 Sep 2015 Last revised: 25 Oct 2016

See all articles by João Caldeira

João Caldeira

Universidade Federal do Rio Grande do Sul (UFRGS)

Guilherme V. Moura

Universidade Federal de Santa Catarina (UFSC) - Department of Economics

Francisco J. Nogales

Universidad Carlos III de Madrid - Department of Statistics; Institute of Financial Big Data UC3M-BS

Andre A. P. Santos

Universidade Federal de Santa Catarina (UFSC) - Department of Economics

Date Written: September 22, 2015

Abstract

We devise a novel approach to combine predictions of high dimensional conditional covariance matrices using economic criteria based on portfolio selection. The combination scheme takes into account not only the portfolio objective function but also the portfolio characteristics in order to define the mixing weights. Three important advantages are that i) it does not require a proxy for the latent conditional covariance matrix, ii) it does not require optimization of the combination weights, and iii) can be calibrated in order to adjust the influence of the best performing models. Empirical application involving a data set with 50 assets over a 10-year time span shows that the proposed economic-based combinations of multivariate volatility forecasts leads to mean-variance portfolios with higher risk-adjusted performance in terms of Sharpe ratio as well as to minimum variance portfolios with lower risk on an out-of-sample basis with respect to a number of benchmark specifications.

Keywords: Composite likelihood, conditional correlation models, model confidence set, realized covariance, stochastic volatility

JEL Classification: C53, E43, G17

Suggested Citation

Caldeira, João and Moura, Guilherme Valle and Nogales, Francisco J. and A. P. Santos, Andre, Combining Multivariate Volatility Forecasts: An Economic-Based Approach (September 22, 2015). Journal of Financial Econometrics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2664128 or http://dx.doi.org/10.2139/ssrn.2664128

João Caldeira

Universidade Federal do Rio Grande do Sul (UFRGS) ( email )

Av. Carlos Gomes 1111
Porto Alegre, Rio Grande do Sul 90480-004
Brazil

Guilherme Valle Moura

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

Francisco J. Nogales

Universidad Carlos III de Madrid - Department of Statistics ( email )

Avda. de la Universidad, 30
Leganes, Madrid 28911
Spain
+34 916248773 (Phone)

HOME PAGE: http://www.est.uc3m.es/Nogales

Institute of Financial Big Data UC3M-BS ( email )

CL. de Madrid 126
Madrid, Madrid 28903
Spain

Andre A. P. Santos (Contact Author)

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

HOME PAGE: http://sites.google.com/site/andreportela

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