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基于Copula的最小方差套期保值比率 被引量:30

Minimum variance hedge ratio based on Copula
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摘要 提出了套期保值的期货与现货非线性匹配原理和收益率波动预测原理,在最小方差套期保值模型的基础上,借助Copula模型计算体现非线性相关的相关系数,利用GARCH和EWMA模型对期货和现货的标准差进行预测,提高套期保值的有效性.该模型的特点一是利用Copula函数计算中位数相关系数,实现了期货与现货收益率的非线性匹配,保证了当期货价格和现货价格发生较大波动时的相关系数计算的准确性.二是通过套期保值的收益率波动原理,利用GARCH模型、EWMA模型对期货和现货的标准差进行预测,解决了因套期保值之前和套期保值期间收益率波动发生结构性变化所导致的套期保值效果失真的问题.实证结果表明,该研究模型的有效性高于现有研究计算的套期保值比率.利用该模型进行套期保值可以更有效的规避现货价格风险. On the base of minimum variance hedge ratio, this paper put forward principle of nonlinear matching of futures and cashes, and the one of return variance anticipation, using Copula model to calculate the nonlinear correlation, and using GARCH and EWMA model to anticipate the standard deviation of futures' and cashes' return rate, so the hedge efficiency will be enhanced. The character of the model is firstly that using the copula to calculate the correlation parameter to matching the futures' and cashes' return rate nonlinearly, so the calculation of correlation parameter in extreme condition will be guaranteed. Secondly, through the principle of return variance anticipation, we use GARCH and EWMA model to anticipate the standard deviation of futures' and cashes' return rate, thus we can solve efficiency distortion when the return rate of futures and cashes structure changing. Empirical test shows that, the efficiency of this model is higher than present ones. Using the this paper's model to hedge can effectively averse cash risk.
出处 《系统工程理论与实践》 EI CSCD 北大核心 2009年第8期1-10,共10页 Systems Engineering-Theory & Practice
基金 国家自然科学基金(70571010) 中期协联合研究计划(GT200410 ZZ200505) 大连市科技计划项目(2004C1ZC227)
关键词 最小方差 套期保值 COPULA GARCH EWMA minimum variance hedge ratio Copula GARCH EWMA
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  • 1徐海熙.论期货市场价格变动原因[J].江苏经贸职业技术学院学报,2004(1):32-35. 被引量:2
  • 2[1]Nelsen, R. B (1998), An Introduction to Copulas, Lectures Notes in Statistics, 139,Springer Verlag, New York.
  • 3[2]Embrechts, P., Lindskog, F. And McNeil, A. (2001), Modelling Dependence with Copulas and Applications to Risk Management. Dept. of Math. CH-8092, Zürich, Switzerland.
  • 4[3]Bouyé, E. (2000), Copulas for Finance, A Reading Guide and Some Applications. City University Business School,London.
  • 5[10]Sklar A. Fonctions de repartition àn dimensions et leurs marges[J]. Publication de 1' Institut de Statistique de 1' Université de Paris, 1959, 8: 229-231.
  • 6[11]Nelsen R B. An Introduction to Copulas[M]. New York: Springer, 1998.
  • 7[12]Frees E W, Valdez E A. Understanding relationships using copulas[J]. North American Actuarial Journal, 1998, 2 (1): 1-25.
  • 8[13]Bouyé E, Durrleman V, Nikeghbali A, et al. Copulas for Finance: A Reading Guide and Some Applications[ R]. London: Financial Econometrics Research Centre, City University Business School, 2000.
  • 9[14]Patton A J. Estimation of Copula models for Time Series of Possibly Different Lengths[ R ]. San Diego: Department of Economics,University of California, 2001.
  • 10[15]Diebold F X, Gunther T, Tay A S. Evaluating density forecasts with applications to financial risk management[J]. International Economic Review, 1998, 39: 863-883.

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