An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

TL;DR

This paper evaluates a dynamic multiscale hedging approach using wavelet decomposition, demonstrating its effectiveness in reducing variance and tail risk across different time scales for various assets.

Contribution

It introduces a wavelet-based dynamic hedging model that adapts to multiple time scales, enhancing hedge effectiveness and understanding residual tail risks.

Findings

Variance reduction improves at longer scales

Residual tail risk persists across all scales

Hedge effectiveness varies with time horizon

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.