GARCH model is being widely used in the research of hedging. However, in some recent empirical analysis, the application in estimation of hedging ratio of this model has been proved to be defective.The high persistence of conditional variance in GARCH model affect the accuracy of the description of asset price series. The Markov Regime-Switching is applied into the construction of hedging model in this paper.The MRS-DCC model which combines MRS model with DCC-GARCH model is established. Using this new model, the hedging ratio of copper futures market is estimated. Meantime, the range yields instead of intraday yields is used as the mark yields to estimate the hedging ratio innovatively. Range yields can reflect the variance of target asset price accurately and help the investor hold the risk of changing of margin position. The using of range yields conform to the real demand of hedging strategy. The method of combination of MRS model and DCC-GARCH model is explained theoretically. With the empirical analysis of copper futures market,from October 15, 2007 to October 15, 2010, in both in-sample and out-sample method, it is proved that the introduction of the Markov regime-switching and range yields improves the accuracy of the estimation of hedging ratio and the hedging performance.The reference for state dependent hedging strategy and measurement of volatility risk of asset price are prouided.
PENG Hong-feng, CHEN Yi
. The Estimation of Optimal Hedging Ratioof Copper Future Market of China——Based on Markov Regime-Switching GARCH Model[J]. Chinese Journal of Management Science, 2015
, 23(5)
: 14
-22
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.05.003
[1] Johnson L L. The theory of hedging and speculation in commodity futures[J]. Risk Uncertainty & Profit, 1960, 27(2):297-298.
[2] Ederington L H. The hedging performance of the new futures markets[J]. Journal of Finance, 1979, 34(1):157 -170.
[3] Park H Y, Bera A K. Interest-rate volatility, basis risk and heteroscedasticity in hedging mortgages[J]. Real Estate Economics, 1987, volume 15(2):79-97(19).
[4] Engle R. Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation [J].Econometrica,1982(4):987-1007.
[5] Bollerslev T. Modelling the coherence in short-run nominal exchange rates: a multivariate generalized arch model[J].The Review of Economics and Statistics,1990,72(3):498-505.
[6] Tse Y K, Tsui A K C. A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations[J].Journal of Business & Economic Statistics,2002(3):351-362.
[7] Lamoureux C G, Lastrapes W D.Persistence in variance, structural change, and the GARCH model[J].Journal of Business & Economic Statistics,1990(2):225-234.
[8] Fong W M, See K H.A markov switching model of the conditional volatility of crude oil futures prices[J].Energy Economics,2002:71-95.
[9] Alizadeh A H, Nomikos N K. A markov regime switching approach for hedging stock indices[J].The Journal of Futures Markets,2004(7):649-674.
[10] Lee H T, Yoder J. Optimal hedging with a regime-switching time-varying correlation GARCH model [J].The Journal of Futures Markets,2007(5): 495-516.
[11] Lee H T, Yoder J.A bivariate markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios[J].Applied Economics,2007:1253-1265.
[12] 付胜华,檀向球.股指期货套期保值研究及其实证分析[J].金融研究,2009(4):113-119.
[13] 付剑茹,张宗成. 时变最优套期保值比估计及比较研究——基于卡尔曼滤波在状态空间模型中的应用[J].管理科学学报,2010(12):23-33.
[14] 彭红枫,叶永刚.基于修正的ECM-GARCH模型的动态最优套期保值比率估计及比较研究[J].中国管理科学,2007(5):29-35.
[15] Kroner K F, Sultan J. Time-varying distributions and dynamic hedging with foreign currency futures 1993(28).
[16] 王辉,孙志凌,谢幽篁.中国农产品期货套期保值非对称效应研究[J].统计研究,2012(7):68-74.
[17] 赵鹏, 曾剑云. 我国股市周期性破灭型投机泡沫实证研究——基于马尔可夫区制转换方法[J]. 金融研究, 2008, 25(4):174-187.
[18] 朱孟楠,刘林,倪玉娟.人民币汇率与我国房地产价格——基于Markov区制转换VAR模型的实证研究[J].金融研究,2011(5):58-71.
[19] Parkinson M. The extreme value method for estimating the variance of the rate of return[J].The Journal of Business,1980(1):61-65.
[20] Andersen T G, Bollerslev T. Answering the skeptics: yes, standard volatility models do provide accurate forecasts[J]. International Economic Review, 1998, 39(4):885-905.
[21] Mapa, Jones D S. A range-based GARCH model for forecasting volatility [J].The Philippine Review of Economics,2003(2):73-90.
[22] Gray S F. Modeling the conditional distribution of interest rates as a regime-switching process[J]. Journal of Financial Economics, 1996, 42(1):27-62.
