基于时变Markov的DCC-GARCH模型最小风险套期保值研究

doi:10.16381/j.cnki.issn1003-207x.2020.10.002

中国管理科学 ›› 2020, Vol. 28 ›› Issue (10): 13-23.doi: 10.16381/j.cnki.issn1003-207x.2020.10.002

基于时变Markov的DCC-GARCH模型最小风险套期保值研究

王佳^1,2, 金秀², 王旭³, 李刚¹

1. 东北大学秦皇岛分校经济学院, 河北秦皇岛 066004;
2. 东北大学工商管理学院, 辽宁沈阳 110819;
3. 河北环境工程学院经济学院, 河北秦皇岛 066102

收稿日期:2018-09-13 修回日期:2019-09-05 出版日期:2020-10-20 发布日期:2020-11-11
通讯作者: 王佳(1986-),女(汉族),河北唐山人,东北大学秦皇岛分校经济学院,讲师,东北大学博士后,研究方向:金融工程,E-mail:wangjia@neuq.edu.cn. E-mail:wangjia@neuq.edu.cn
基金资助:
国家自然科学基金资助项目（71601040，71571041，71971051，71601041）；河北省自然科学基金资助项目（G2019501086）；中国博士后科学基金资助项目（2018M631797）；河北省高等学校社科研究基金资助项目（SD202007）；东北大学博士后基金资助项目（20190315）

Research on Variance Minimization Hedging Based on Time-Varying Markov DCC-GARCH Model

WANG Jia^1,2, JIN Xiu², WANG Xu³, LI Gang¹

1. School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
2. College of Business Administration, Northeastern University, Shenyang 110819, China;
3. College of Economics, Hebei University of Environmental Engineering, Qinhuangdao 066102, China

Received:2018-09-13 Revised:2019-09-05 Online:2020-10-20 Published:2020-11-11

摘要/Abstract

摘要： 考虑Markov状态转移概率的时变特征，在传统DCC-GARCH基础上，提出基于Markov时变转移概率的DCC-GARCH模型（TVTP-DCC-GARCH）研究最小风险套期保值比例的估计方法，并利用两阶段极大似然法对模型参数进行估计。进一步分别从样本内和样本外估计沪深300指数期货和现货的最优套期保值比率，对套期保值的绩效进行检验，并将检验结果分别与Markov转移概率恒定的DCC-GARCH（FTP-DCC-GARCH）、DCC-GARCH、OLS、1：1完全套期保值以及无套期保值的沪深300指数现货的绩效进行对比。实证结果表明，利用基于Markov状态转移的DCC-GARCH模型研究沪深300指数期货的套期保值问题具有一定合理性，且在参数估计中TVTP-DCC-GARCH模型的拟合效果最佳；在套期保值有效性方面，TVTP-DCC-GARCH模型优于其他模型，说明在DCC-GARCH模型中引入时变状态转移概率能够有效提高套期保值组合的绩效。

关键词: 时变Markov, 状态转移, 套期保值, DCC-GARCH

Abstract: Stock market fluctuate frequently because of information shocks caused by sudden events resulting from economic, political, or natural disasters. Therefore, hedging the stock market has always been the popular research topic. Futures market, as an important part of the financial markets, is always used to operate hedging strategies in order to realize the risk transfer. The key problem of hedging theory is to determine the optimal hedging ratio. In this study, using a regime switching framework, a new estimation method of minimum risk hedging ratio is proposed. Then, taking the actual data of CSI300 index futures and spot as samples, the hedging ratios are estimated respectively from both in sample and out of sample. Compared with traditional hedging methods, the hedging performance of this new method is tested. This study is of great significance for hedgers to fully understand the hedging rules of futures market and avoid the volatility risk of spot price effectively.
In the first part, considering the time varying characteristic of Markov regime transition probability, based on the traditional DCC-GARCH, a Markov regime switching DCC-GARCH model with time varying transition probability (TVTP-DCC-GARCH) is presented to study on the estimation method of minimum variance hedge ratio. Two-stage maximum likelihood method is used to estimate the parameters of the model. In the second part, with the actual data of CSI300 index futures and spot in sample, the hedging ratios of TVTP-DCC-GARCH is estimated, and the hedging performance is compared with other models, including a MRS-DCC-GARCH with a fixed transition probability (FTP-DCC-GARCH), DCC-GARCH, OLS, naïve hedging strategy and indices spot with no hedging. Furthermore, one-step-ahead forecasts out of sample are produced to forecast the hedging ratios of TVTP-DCC-GARCH and the hedging performance of the above models is checked.
In summary, the DCC-GARCH model based on Markov regime switching is reasonable to study the hedging problem of CSI300 index future, and the TVTP-DCC-GARCH model has the best fitting effect. Thus, it is necessary to build a hedging model based on Markov with time varying transition probability, and explore the impact of time varying transition probability on the optimal hedging ratio in the futures market. In addition, in terms of hedging effectiveness, TVTP-DCC-GARCH model is superior to other models, which means that introducing the time varying transition probability into DCC-GARCH model can effectively improve the performance of hedging portfolio.

Key words: time-varying markov, regime switching, hedging, DCC-GARCH

中图分类号:

F830.9

王佳, 金秀, 王旭, 李刚. 基于时变Markov的DCC-GARCH模型最小风险套期保值研究[J]. 中国管理科学, 2020, 28(10): 13-23.

WANG Jia, JIN Xiu, WANG Xu, LI Gang. Research on Variance Minimization Hedging Based on Time-Varying Markov DCC-GARCH Model[J]. Chinese Journal of Management Science, 2020, 28(10): 13-23.

参考文献

[1] Ederington L H. The hedging performance of the new futures markets[J]. Journal of Finance, 1979, 34:157-170.
[2] Hou Y, Li S. Hedging performance of Chinese stock index futures:An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches[J]. Pacific Basin Finance Journal, 2013, 24(3):109-131.
[3] Kroner K F, Sultan J. Time varying distributions and dynamic hedging with foreign currency futures[J]. Journal of Financial and Quantitative Analysis, 1993, 28(4):535-551.
[4] Chung S K. Bivariate mixed normal GARCH models and out-of-sample hedge performances[J]. Finance Research Letters, 2009, 6(3):130-137.
[5] Chang C Y, Lai J Y, Chuang I Y. Futures hedging effectiveness under the segmentation of bear/bull energy markets[J]. Energy Economics, 2010, 32(2):442-449.
[6] Zhou Jian. Hedging performance of REIT index futures:A comparison of alternative hedge ratio estimation methods[J]. Economic Modelling, 2016, 52(Part B):690-698.
[7] Basher S A, Sadorsky P. Hedging emerging market stock prices with oil, gold, VIX, and bonds:A comparison between DCC, ADCC and GO-GARCH[J]. Energy Economics, 2016, 54:235-247.
[8] Fong W M, See K H. A Markov switching model of the conditional volatility of crude oil futures prices[J]. Energy Economics, 2002, 24(1):71-95.
[9] Markopoulou C E, Skintzi V D, Refenes A P N. Realized hedge ratio:Predictability and hedging performance[J]. International Review of Financial Analysis, 2016, 45:121-133.
[10] Su E. Stock index hedging using a trend and volatility regime-switching model involving hedging cost[J]. International Review of Economics and Finance, 2017, 47:233-254.
[11] Philip D, Shi Y. Optimal hedging in carbon emission markets using Markov regime switching models[J]. Journal of International Financial Markets Institutions and Money, 2016, 43:1-15.
[12] Yan Zhipeng, Li Shenghong. Hedge ratio on Markov regime-switching diagonal Bekk-Garch model[J]. Finance Research Letters, 2018, 24:49-56.
[13] Billio M, Caporin M. Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis[J]. Statistical Methods and Applications, 2005, 14(2):145-161.
[14] Billio M, Casarin R, Osuntuyi A. Markov switching GARCH models for Bayesian hedging on energy futures markets[J]. Energy Economics, 2018, 70:545-562.
[15] 谢赤, 屈敏, 王纲金. 基于M-Copula-GJR-VaR模型的黄金市场最优套期保值比率研究[J]. 管理科学, 2013, 26(2):90-99.
[16] 彭红枫, 陈奕. 中国铜期货市场最优套期保值比率估计——基于马尔科夫区制转移GARCH模型[J]. 中国管理科学, 2015, 23(5):14-22.
[17] 余星, 张卫国, 刘勇军. 基于等价鞅测度的动态套期保值模型研究[J]. 系统工程理论与实践, 2018, 38(2):287-298.
[18] Filardo A. Business-cycle phases and their transitional dynamics[J]. Journal of Business and Economic Statistics, 1994, 12(3):299-308.
[19] Viswanath P V. Efficient use of information, convergence adjustments, and regression estimates of hedge ratios[J]. The Journal of Futures Markets, 1993, 13:43-53.
[20] Simmons-Süer B. How relevant is capital structure for aggregate investment? A regime-switching approach[J]. International Review of Economics and Finance, 2018, 53:109-117.
[21] Gary S F. Modeling the conditional distribution of interest rates as a regime-switching process[J]. Journal of Financial Economics, 1996, 42(1):27-62.
[22] Lee H T, Yoder J K. A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios[J]. Applied Economics, 2007, 39(10):1253-1265.
[23] Hamilton J D. A new approach to the economic analysis of nonstationary time series and the business cycle[J]. Econometrica, 1989, 57(2):357-384.
[24] 苏海军, 欧阳红兵. 危机传染效应的识别与度量——基于改进MIS-DCC的分析[J]. 管理科学学报, 2013, 16(8):20-30.
[25] Grossman S J, Shiller R J. The determinants of the variability of stock market prices[J]. American Economic Review, 1981, 71(2):222-227.

基于时变Markov的DCC-GARCH模型最小风险套期保值研究

Research on Variance Minimization Hedging Based on Time-Varying Markov DCC-GARCH Model

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