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On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios

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  • Yuan-Hung Hsu Ku
  • Ho-Chyuan Chen
  • Kuang-Hua Chen
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    Abstract

    This article applies the dynamic conditional correlation model of Engle (2002) with error correction terms in order to investigate the optimal hedge ratios of British and Japanese currency futures markets. For a comparison, the estimates of three other models -- traditional generalized autoregressive conditional heteroskedasticity (GARCH), ordinary least square (OLS) and error correction model (ECM) -- are also reported. Results show that the dynamic conditional correlation model yields the best hedging performance in both futures markets. Nonetheless, the traditional multivariate GARCH model (which exhibits constant conditional correlations and time-varying hedge ratios) performs the worst hedging effectiveness, even inferior to the time-invariant hedging methods (OLS and ECM). The inclusion of dynamic conditional correlations in the GARCH model can therefore better capture the frequent fluctuations in futures markets.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

    Volume (Year): 14 (2007)
    Issue (Month): 7 ()
    Pages: 503-509

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    Handle: RePEc:taf:apeclt:v:14:y:2007:i:7:p:503-509

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    Cited by:
    1. Chia-Lin Chang & Lydia González-Serrano & Juan-Ángel Jiménez-Martín, 2011. "Currency Hedging Strategies Using Dynamic Multivariate GARCH," Documentos de Trabajo del ICAE 2011-33, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," CIRJE F-Series CIRJE-F-704, CIRJE, Faculty of Economics, University of Tokyo.
    3. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    4. Ikram Jebabli & Mohamed Arouri & Frédéric Teulon, 2014. "On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVPVAR models with stochastic volatility," Working Papers 2014-209, Department of Research, Ipag Business School.
    5. Caporin, Massimiliano & Jimenez-Martin, Juan-Angel & Gonzalez-Serrano, Lydia, 2013. "Currency hedging strategies, strategic benchmarks and the Global and Euro Sovereign financial crises," MPRA Paper 50940, University Library of Munich, Germany, revised 23 Oct 2013.
    6. Асатуров К.Г. & Теплова Т.В., 2014. "Построение Коэффициентов Хеджирования Для Высоколиквидных Акций Российского Рынка На Основе Моделей Класс�," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 37-54, янваÑ.
    7. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    8. Zanotti, Giovanna & Gabbi, Giampaolo & Geranio, Manuela, 2010. "Hedging with futures: Efficacy of GARCH correlation models to European electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 135-148, April.

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