IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1103.4943.html
   My bibliography  Save this paper

An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1103.4943
    File Function: Latest version
    Download Restriction: no

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:ecmode:v:68:y:2018:i:c:p:570-585 is not listed on IDEAS
    2. 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.
    3. repec:spr:jeicoo:v:12:y:2017:i:3:d:10.1007_s11403-016-0176-x is not listed on IDEAS
    4. 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.
    5. 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.
    6. repec:eee:ecmode:v:64:y:2017:i:c:p:312-321 is not listed on IDEAS
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. repec:eee:eneeco:v:66:y:2017:i:c:p:493-507 is not listed on IDEAS
    13. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    14. 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.
    15. repec:eee:finlet:v:21:y:2017:i:c:p:115-125 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1103.4943. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.