The Variance Implied Conditional Correlation

23 Pages Posted: 27 Feb 2018 Last revised: 30 Aug 2018

See all articles by Andres Algaba

Andres Algaba

Vrije Universiteit Brussel (VUB)

Kris Boudt

Vrije Universiteit Brussel; Vrije Universiteit Amsterdam

Steven Vanduffel

Vrije Universiteit Brussel (VUB)

Date Written: August 2, 2018

Abstract

We use univariate GARCH estimations to construct a computationally simple and flexible filter to predict the time-varying correlation matrix of asset returns. The proposed Variance Implied Conditional Correlation (VICC) filter exploits the polarization result that links the correlation between two variables with variances of linear combinations of their standardized values. In a Monte Carlo study, we show that it yields accurate correlation estimates for common choices of correlation dynamics. We further find in an empirical cross hedging application that the use of the VICC leads to substantially more stable hedge ratios which perform at least as well as the standard benchmark models in terms of variance reduction and achieved decorrelation.

Keywords: Conditional Correlation, GARCH, DCC, Cross Hedging, Hedge Ratio, Regularization

JEL Classification: C10, G11

Suggested Citation

Algaba, Andres and Boudt, Kris and Vanduffel, Steven, The Variance Implied Conditional Correlation (August 2, 2018). Available at SSRN: https://ssrn.com/abstract=3125020 or http://dx.doi.org/10.2139/ssrn.3125020

Andres Algaba (Contact Author)

Vrije Universiteit Brussel (VUB) ( email )

Pleinlaan 2
http://www.vub.ac.be/
Brussels, 1050
Belgium

Kris Boudt

Vrije Universiteit Brussel ( email )

Pleinlaan 2
http://www.vub.ac.be/
Brussels, 1050
Belgium

Vrije Universiteit Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

Steven Vanduffel

Vrije Universiteit Brussel (VUB) ( email )

Pleinlaan 2
Brussels, Brabant 1050
Belgium

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

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