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An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

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  • Thomas Conlon
  • John Cotter

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

  • Thomas Conlon & John Cotter, 2011. "An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition," Papers 1103.4943, arXiv.org.
    • Handle: RePEc:arx:papers:1103.4943
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    References listed on IDEAS

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    1. Christie-David, Rohan & Chaudhry, Mukesh, 2001. "Coskewness and cokurtosis in futures markets," Journal of Empirical Finance, Elsevier, vol. 8(1), pages 55-81, March.
    2. Kim, Sangbae & In, Francis, 2005. "The relationship between stock returns and inflation: new evidence from wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 435-444, June.
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    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

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