An Empirical Analysis of Dynamic Multiscale Hedging Using Wavelet Decomposition

Journal of Futures Markets, 32 (3):272-299

28 Pages Posted: 10 Feb 2011 Last revised: 10 Dec 2014

Thomas Conlon

University College Dublin

John Cotter

University College Dublin; Anderson School of Management

Date Written: February 7, 2011

Abstract

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.

Suggested Citation

Conlon, Thomas and Cotter, John, An Empirical Analysis of Dynamic Multiscale Hedging Using Wavelet Decomposition (February 7, 2011). Journal of Futures Markets, 32 (3):272-299. Available at SSRN: https://ssrn.com/abstract=1756800 or http://dx.doi.org/10.2139/ssrn.1756800

Thomas Conlon (Contact Author)

University College Dublin ( email )

Smurfit Graduate Business School
Blackrock
Co. Dublin, n/a
Ireland

HOME PAGE: http://www.ucd.ie/bankingfinance/staff/drthomasconlon/

John Cotter

Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

University College Dublin ( email )

School of Business, Carysfort Avenue
Blackrock, Co. Dublin
Ireland
353 1 716 8900 (Phone)
353 1 283 5482 (Fax)

HOME PAGE: http://www.ucd.ie/bankingfinance/staff/professorjohncotter/

Paper statistics

Downloads
78
Rank
257,764
Abstract Views
422