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An empirical analysis of dynamic multiscale hedging using wavelet decomposition
Author(s) -
Conlon Thomas,
Cotter John
Publication year - 2012
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.20519
Subject(s) - kurtosis , econometrics , hedge , wavelet , metric (unit) , portfolio , downside risk , mathematics , value at risk , residual , replicating portfolio , statistics , portfolio optimization , economics , computer science , risk management , financial economics , algorithm , ecology , operations management , management , artificial intelligence , biology
This study 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 scale‐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.