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Currency Hedging Strategies Using Dynamic Multivariate GARCH

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This paper examines the effectiveness of using futures contracts as hedging instruments of: (1) alternative models of volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts. For this purpose, daily data of futures and spot exchange rates of three major international currencies, Euro, British pound and Japanese yen, against the American dollar, are used to analyze hedge ratios and hedging effectiveness resulting from using two different maturity currency contracts, near-month and next-to-near-month contract. Following Chang et al. [17], we estimate four multivariate volatility models (namely CCC, VARMA-AGARCH, DCC and BEKK), and calculate optimal portfolio weights and optimal hedge ratios to identify appropriate currency hedging strategies. The hedging effectiveness index suggests that the best results in terms of reducing the variance of the portfolio are for the USD/GBP exchange rate. The empirical results show that futures hedging strategies are slightly more effective when the near-month future contract is used for the USD/GBP and USD/JPY currencies. Moreover, the CCC and AGARCH models provide similar hedging effectiveness, which suggests that dynamic asymmetry may not be crucial empirically, although some differences appear when the DCC and BEKK models are used.

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  • Chia-Lin Chang & Lydia González-Serrano & Juan-Ángel Jiménez-Martín, 2012. "Currency Hedging Strategies Using Dynamic Multivariate GARCH," Documentos de Trabajo del ICAE 2012-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Feb 2012.
  • Handle: RePEc:ucm:doicae:1207
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    Cited by:

    1. Lv, Fei & Yang, Chen & Fang, Libing, 2020. "Do the crude oil futures of the Shanghai International Energy Exchange improve asset allocation of Chinese petrochemical-related stocks?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    2. David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, Open Access Journal, vol. 4(1), pages 1-14, March.
    3. Martínez, Beatriz & Torró, Hipòlit, 2018. "Hedging spark spread risk with futures," Energy Policy, Elsevier, vol. 113(C), pages 731-746.
    4. John Francis Diaz & Peh Ying Qian & Genevieve Liao Tan, 2018. "Variance Persistence in the Greater China Region: A Multivariate GARCH Approach," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 23(2), pages 49-68, July-Dec.
    5. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    6. Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República - ESPE, vol. 35(84), pages 260-266, December.
    7. Chia-Lin Chang & David E. Allen & Michael McAleer & Ju-Ting Tang & Teodosio Pérez Amaral, 2013. "Risk Modelling and Management: An Overview," Documentos de Trabajo del ICAE 2013-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    8. Yu, Xing & Zhang, Wei Guo & Liu, Yong Jun & Wang, Xinxin & Wang, Chao, 2020. "Hedging the exchange rate risk for international portfolios," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 173(C), pages 85-104.
    9. repec:gam:jrisks:v:4:y:2016:i:1:p:7:d:65863 is not listed on IDEAS
    10. 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.
    11. Chia-Lin Chang & Chia-Ping Liu & Michael McAleer, 2016. "Volatility Spillovers for Spot, Futures, and ETF Prices in Energy and Agriculture," Tinbergen Institute Discussion Papers 16-046/III, Tinbergen Institute.
    12. Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 35(84), pages 260-266, December.
    13. Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República - ESPE, vol. 35(84), pages 260-266, December.
    14. Andre Assis de Salles, 2013. "An Investigation of Some Hedging Strategies for Crude Oil Market," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 51-59.
    15. 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.
    16. Chkili, Walid, 2016. "Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, vol. 38(C), pages 22-34.
    17. 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.

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    More about this item

    Keywords

    Multivariate GARCH; conditional correlations; exchange rates; optimal hedge ratio; optimal portfolio weights; hedging strategies.;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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