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Calculating the optimal hedge ratio: constant, time varying and the Kalman Filter approach

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  • Abdulnasser Hatemi-J
  • Eduardo Roca

Abstract

A crucial input in the hedging of risk is the optimal hedge ratio - defined by the relationship between the price of the spot instrument and that of the hedging instrument. Since it has been shown that the expected relationship between economic or financial variables may be better captured by a time varying parameter model rather than a fixed coefficient model, the optimal hedge ratio, therefore, can be one that is time varying rather than constant. This study suggests and demonstrates the use of the Kalman Filter approach for estimating time varying hedge ratio - a procedure that is statistically more efficient and with better forecasting properties.

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  • Abdulnasser Hatemi-J & Eduardo Roca, 2006. "Calculating the optimal hedge ratio: constant, time varying and the Kalman Filter approach," Applied Economics Letters, Taylor & Francis Journals, vol. 13(5), pages 293-299.
  • Handle: RePEc:taf:apeclt:v:13:y:2006:i:5:p:293-299 DOI: 10.1080/13504850500365848
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    Cited by:

    1. John Hua Fan & Eduardo Roca & Alexandr Akimov, 2010. "Hedging With Futures Contract: Estimation and Performance Evaluation of Optimal Hedge Ratios in the European Union Emissions Trading Scheme," Discussion Papers in Finance finance:201009, Griffith University, Department of Accounting, Finance and Economics.
    2. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    3. Pok, Wee Ching & Poshakwale, Sunil S. & Ford, J.L., 2009. "Stock index futures hedging in the emerging Malaysian market," Global Finance Journal, Elsevier, vol. 20(3), pages 273-288.
    4. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    5. Abdulnasser Hatemi-J & Eduardo Roca, 2010. "Estimating Optimal Hedge Ratio with Unknown Structural Breaks," Discussion Papers in Finance finance:201010, Griffith University, Department of Accounting, Finance and Economics.
    6. Lubnau, Thorben, 2014. "Spread trading strategies in the crude oil futures market," Discussion Papers 353, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    7. Gupta, R. & Donleavy, G.D., 2009. "Benefits of diversifying investments into emerging markets with time-varying correlations: An Australian perspective," Journal of Multinational Financial Management, Elsevier, pages 160-177.
    8. Marcel Prokopczuk & Yingying Wu, 2013. "Estimating term structure models with the Kalman filter," Chapters,in: Handbook of Research Methods and Applications in Empirical Finance, chapter 4, pages 97-113 Edward Elgar Publishing.

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