Modeling Exchange Rate Dependence Dynamics at Different Time Horizons

Posted: 10 Sep 2010 Last revised: 7 Mar 2011

See all articles by Alexandra Dias

Alexandra Dias

University of York

Paul Embrechts

Swiss Federal Institute of Technology Zurich; Swiss Finance Institute

Date Written: September 8, 2010

Abstract

Despite an extensive body of research, the best way to model the dependence of exchange rates remains an open question. In this paper we present a new approach which employs a flexible time-varying copula model. It allows the conditional correlation between exchange rates to be both time-varying and modeled independently from the marginal distributions. We introduce a dynamic specification for the correlation using the Fisher transformation. Applied to Euro/US dollar and Japanese Yen/US dollar, our results reveal a significantly time-varying correlation, dependent on the past return realizations. We find that a time-varying copula with the proposed correlation specification gives better results than alternative dynamic benchmark models. The dynamic copula model outperforms at six different time horizons, ranging from hourly to daily, confirming the model specification.

Keywords: Foreign exchange rates, Multivariate time series, Copula-GARCH, Conditional dependence, Dynamic copula

JEL Classification: C32, C50, F31

Suggested Citation

Dias, Alexandra and Embrechts, Paul, Modeling Exchange Rate Dependence Dynamics at Different Time Horizons (September 8, 2010). Journal of International Money and Finance, Vol. 29, pp. 1687-1705, 2010, Available at SSRN: https://ssrn.com/abstract=1674085

Alexandra Dias (Contact Author)

University of York ( email )

Freboys Lane
Heslington
York, North Yorkshire YO10 5DD
United Kingdom

Paul Embrechts

Swiss Federal Institute of Technology Zurich ( email )

ETH-Zentrum
CH-8092 Zurich
Switzerland

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
955
PlumX Metrics