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Dynamic futures hedging in currency markets

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  • Atreya Chakraborty
  • John Barkoulas

Abstract

The hedging effectiveness of dynamic strategies is compared with static (traditional) ones using futures contracts for the five leading currencies. The traditional hedging model assumes time invariance in the joint distribution of spot and futures price changes thus leading to a constant optimal hedge ratio (OHR). However, if this time-invariance assumption is violated, time-varying OHRs are appropriate for hedging purposes. A bivariate GARCH model is employed to estimate the joint distribution of spot and futures currency returns and the sequence of dynamic (time-varying) OHRs is constructed based upon the estimated parameters of the conditional covariance matrix. The empirical evidence strongly supports time-varying OHRs but the dynamic model provides superior out-of-sample hedging performance, compared to the static model, only for the Canadian dollar.

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  • Atreya Chakraborty & John Barkoulas, 1999. "Dynamic futures hedging in currency markets," The European Journal of Finance, Taylor & Francis Journals, vol. 5(4), pages 299-314.
    • Handle: RePEc:taf:eurjfi:v:5:y:1999:i:4:p:299-314
    DOI: 10.1080/135184799336975
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    Cited by:

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    2. Aleksander Olstad & George Filis & Stavros Degiannakis, 2021. "Oil and currency volatilities: Co‐movements and hedging opportunities," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2351-2374, April.
    3. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
    4. Caporin, Massimiliano & Jimenez-Martin, Juan-Angel & Gonzalez-Serrano, Lydia, 2014. "Currency hedging strategies in strategic benchmarks and the global and Euro sovereign financial crises," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 159-177.
    5. Chang, Chiao-Yi & Lai, Jing-Yi & Chuang, I-Yuan, 2010. "Futures hedging effectiveness under the segmentation of bear/bull energy markets," Energy Economics, Elsevier, vol. 32(2), pages 442-449, March.
    6. Wei-Han Liu, 2014. "Optimal hedge ratio estimation and hedge effectiveness with multivariate skew distributions," Applied Economics, Taylor & Francis Journals, vol. 46(12), pages 1420-1435, April.
    7. Chi Keung Marco Lau & Mehmet Huseyin Bilgin, 2013. "Hedging with Chinese Aluminum Futures: International Evidence with Return and Volatility Spillover Indices Under Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S1), pages 37-48, January.
    8. Liu, Xiaochun & Jacobsen, Brian, 2011. "The Dynamic International Optimal Hedge Ratio," MPRA Paper 35260, University Library of Munich, Germany.
    9. Su, Yongyang & Lau, Chi Keung Marco & Tan, Na, 2013. "Hedging China’s Energy Oil Market Risks," MPRA Paper 47134, University Library of Munich, Germany.
    10. Асатуров К.Г. & Теплова Т.В., 2014. "Построение Коэффициентов Хеджирования Для Высоколиквидных Акций Российского Рынка На Основе Моделей Класса Garch," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 37-54, январь.
    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. Yan Hu & Jian Ni, 2024. "A deep learning‐based financial hedging approach for the effective management of commodity risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 879-900, June.

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