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Wavelet Estimation of Asymmetric Hedge Ratios: Does Econometric Sophistication Boost Hedging Effectiveness?


  • Elizabeth A. Maharaj

    (Department of Econometrics and Business Statistics, Monash University, Australia)

  • Imad Moosa

    (Department of Accounting and Finance, Monash University, Australia)

  • Jonathan Dark

    (Department of Finance, University of Melbourne, Australia)

  • Param Silvapulle

    (Department of Econometrics and Business Statistics, Monash University, Australia)


This paper utilises wavelet analysis, which is becoming popular in economics and finance, to estimate the hedge ratios for spot positions on the West Texas Intermediate crude oil, soybeans and the S&P500 index. This technique is combined with a two-stage regime switching threshold model to estimate asymmetric hedge ratios corresponding to positive and negative returns on futures contracts. Other simple and sophisticated techniques are also used as a benchmark for the purpose of comparison, including the naive model and the asymmetric error correction GJR-GARCH model. On the basis of the variance ratio test and variance reduction, it is revealed that econometric sophistication does not boost hedging effectiveness.

Suggested Citation

  • Elizabeth A. Maharaj & Imad Moosa & Jonathan Dark & Param Silvapulle, 2008. "Wavelet Estimation of Asymmetric Hedge Ratios: Does Econometric Sophistication Boost Hedging Effectiveness?," International Journal of Business and Economics, College of Business and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 7(3), pages 213-230, December.
  • Handle: RePEc:ijb:journl:v:7:y:2008:i:3:p:213-230

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    References listed on IDEAS

    1. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
    2. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(04), pages 535-551, December.
    3. Ramsey, James B. & Lampart, Camille, 1998. "Decomposition Of Economic Relationships By Timescale Using Wavelets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(01), pages 49-71, March.
    4. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    5. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
    6. Chen, Sheng-Syan & Lee, Cheng-few & Shrestha, Keshab, 2003. "Futures hedge ratios: a review," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 433-465.
    7. Ripple, Ronald D. & Moosa, Imad A., 2009. "The effect of maturity, trading volume, and open interest on crude oil futures price range-based volatility," Global Finance Journal, Elsevier, vol. 20(3), pages 209-219.
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    Cited by:

    1. Mara Madaleno & Carlos Pinho, 2010. "Hedging Performance and Multiscale Relationships in the German Electricity Spot and Futures Markets," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 3(1), pages 1-37, December.

    More about this item


    asymmetric hedge ratios; variance ratio; variance reduction; wavelets;

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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