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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.
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Suggested Citation

  • Thomas Conlon & John Cotter, 2012. "An empirical analysis of dynamic multiscale hedging using wavelet decomposition," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(3), pages 272-299, March.
  • Handle: RePEc:wly:jfutmk:v:32:y:2012:i:3:p:272-299
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    1. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. " Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    2. Kim, In Joon & Kim, Sol, 2004. "Empirical comparison of alternative stochastic volatility option pricing models: Evidence from Korean KOSPI 200 index options market," Pacific-Basin Finance Journal, Elsevier, vol. 12(2), pages 117-142, April.
    3. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters,in: Theory Of Valuation, chapter 8, pages 229-288 World Scientific Publishing Co. Pte. Ltd..
    4. Ait-Sahalia, Yacine, 1996. "Nonparametric Pricing of Interest Rate Derivative Securities," Econometrica, Econometric Society, vol. 64(3), pages 527-560, May.
    5. Christoffersen, Peter & Jacobs, Kris, 2004. "The importance of the loss function in option valuation," Journal of Financial Economics, Elsevier, pages 291-318.
    6. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 2000. "Pricing and hedging long-term options," Journal of Econometrics, Elsevier, pages 277-318.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. Roger W. Lee, 2004. "The Moment Formula For Implied Volatility At Extreme Strikes," Mathematical Finance, Wiley Blackwell, vol. 14(3), pages 469-480.
    9. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    10. Frey, Rüdiger, 1997. "Derivative Asset Analysis in Models with Level-Dependent and Stochastic Volatility," Discussion Paper Serie B 401, University of Bonn, Germany.
    11. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., pages 217-244.
    12. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    13. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
    14. Alexander, Carol & Nogueira, Leonardo M., 2007. "Model-free hedge ratios and scale-invariant models," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1839-1861, June.
    15. Bliss, Robert R. & Panigirtzoglou, Nikolaos, 2002. "Testing the stability of implied probability density functions," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 381-422, March.
    16. Steven A. Weinberg, 2001. "Interpreting the volatility smile: an examination of the information content of option prices," International Finance Discussion Papers 706, Board of Governors of the Federal Reserve System (U.S.).
    17. Stephane Crepey, 2004. "Delta-hedging vega risk?," Quantitative Finance, Taylor & Francis Journals, vol. 4(5), pages 559-579.
    18. Rolf Poulsen & Klaus Reiner Schenk-Hoppe & Christian-Oliver Ewald, 2009. "Risk minimization in stochastic volatility models: model risk and empirical performance," Quantitative Finance, Taylor & Francis Journals, vol. 9(6), pages 693-704.
    19. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    20. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1998. "Implied Volatility Functions: Empirical Tests," Journal of Finance, American Finance Association, vol. 53(6), pages 2059-2106, December.
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    Cited by:

    1. repec:eee:ecmode:v:64:y:2017:i:c:p:312-321 is not listed on IDEAS
    2. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    3. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    4. Bredin, Don & Conlon, Thomas & Potì, Valerio, 2015. "Does gold glitter in the long-run? Gold as a hedge and safe haven across time and investment horizon," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 320-328.
    5. Hou, Yang & Holmes, Mark, 2017. "On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging," MPRA Paper 82000, University Library of Munich, Germany.
    6. Dai, Jun & Zhou, Haigang & Zhao, Shaoquan, 2017. "Determining the multi-scale hedge ratios of stock index futures using the lower partial moments method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 502-510.
    7. repec:eee:finlet:v:21:y:2017:i:c:p:115-125 is not listed on IDEAS
    8. repec:spr:jeicoo:v:12:y:2017:i:3:d:10.1007_s11403-016-0176-x is not listed on IDEAS
    9. Kim, Myeong Jun & Park, Sung Y., 2016. "Optimal conditional hedge ratio: A simple shrinkage estimation approach," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 139-156.
    10. Wang, Gang-Jin & Xie, Chi & He, Ling-Yun & Chen, Shou, 2014. "Detrended minimum-variance hedge ratio: A new method for hedge ratio at different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 70-79.
    11. Kotkatvuori-Örnberg, Juha, 2016. "Dynamic conditional copula correlation and optimal hedge ratios with currency futures," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 60-69.
    12. Anindya Chakrabarty & Anupam De & Gautam Bandyopadhyay, 2016. "Horizon heterogeneity, institutional constraint and managerial myopia: a multi-frequency perspective on ELSS," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 9(1), pages 18-47.
    13. Bredin, Don & Conlon, Thomas & Potì, Valerio, 2017. "The price of shelter - Downside risk reduction with precious metals," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 48-58.
    14. repec:eee:eneeco:v:66:y:2017:i:c:p:493-507 is not listed on IDEAS

    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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