典型事实、混合Copula函数与金融市场相依结构研究

doi:10.16381/j.cnki.issn1003-207x.2015.04.003

Abstract

Abstract: The ARFIMA-FIAPARCH-skst model is applied to establish return-volatility model to CSI300 and HIS. Then the parameter estimated is combined to revise model to confine the final model and get rid of the effect of dependence relation from stylized facts in financial market. And then the mixed copula model made of Clayton, Frank and Gumbel is applied to establish a model of dependence structure. The result of the research indicates that evident of leverage effects found by the existing research hasn't been observed by local market and HongKong market. Moreover, the mixed copula model made of Clayton, Frank and Gumbel can describe the dependence structure between two markets accurately and the dependence relation of lower tail of two markets is stronger than the dependence relation of upper tail. Besides, the time varying mixed-copula also indicates that there is an obvious asymmetric dependence relationship.

Key words: financial market, stylized facts, mixed copula, dependence structure

CLC Number:

F830.2

LIN Yu, CHEN Wang, WANG Yi-ming, HUANG Xun. Study on Dependency Structure of Financial Markets Based on the Stylized Facts and Mixed Copula Function[J]. Chinese Journal of Management Science, 2015, 23(4): 20-29.

References

[1] Cont R. Empirical properties of assets returns:Stylized facts and statistical issues [J]. Quantitative Finance, 2001, 1:223-236.

[2] 林宇. 典型事实、极值理论与金融市场动态风险测度研究 [J]. 投资研究, 2012, 31(1):41-56.

[3] King M, Wadhwani S. Transmission of volatility between stock markets[J]. Review of Financial Studies, 1990, 3(1):5-33.

[4] Longin F, Bruno S. Is the correlation in international equity returns constant:1960-1990[J]. Journal of International Money and Finance.1995, 14(1):3-26.

[5] 林宇.中国与部分国际股市动态极值风险传导效应研究[J]. 中国管理科学, 2008, 16(4):36-43.

[6] 陈王, 魏宇, 淳伟德, 等.中国股市与周边股市波动风险传导效应研究[J]. 中国管理科学, 2011, 19(6):31-39.

[7] Hong Yongmiao, Liu Yanhui, Wang Shauyang. Granger causality in risk and detection of extreme risk spillover between financial markets[J]. Journal of Econometrics, 2009, 150(2):271-287.

[8] 易文德.基于copula理论的金融风险相依结构模型及应用 [M]. 北京:中国经济出版社, 2011.

[9] Nelsen R B. An introduction to copulas[M]. Berlin:Springer, 1999.

[10] Bouyé E, Durrleman V, Nikeghbali A, et al. Copulas for finance:A reading guide and some applications[R]. Working Paper, Financial Econometrics Research Center, City University Business School, London, 2000.

[11] 韦艳华, 张世英. Copula 理论及其在金融分析上的应用 [M]. 北京:清华大学出版社, 2008.

[12] Yi Wende, Lao Shaoyi. Statistical properties of parametric estimators for Markov chain vectors based on copula models [J]. Journal of statistical Planning and Inference, 2010, 140(6):1465-1480.

[13] 易文德.基于ARMA-GARCH-COUPULA模型的交易量与股价波动相依关系[J]. 系统管理学报, 2012, 21(5):696-703.

[14] 林宇, 陈王. 基于典型事实的金融市场动态极值风险测度与传导效应研究 [M]. 北京:科学出版社, 2013.

[15] 吴吉林. 基于机制转换copula模型的股市量价尾部关系研究[J]. 中国管理科学, 2012, 20(5):16-23.

[16] 吴吉林, 张二华. 基于机制转换混合copula模型的我国股市间极值相依性[J]. 系统工程理论与实践, 2012, 32(8):1662-1672.

[17] 韦艳华, 张世英. 基于copula 函数的金融市场尾部相关性分析[J]. 管理学报, 2005, 2(5):601-605.

[18] 任仙玲, 张世英. 基于copula函数的金融市场尾部相关性分析[J]. 统计与信息论坛, 2008, 23(6):66-71.

[19] 闫海梅, 王波. 沪深300指数与沪深股市尾部相关性分析[J]. 数学的实践与认识, 2010, 40(22):50-55.

[20] Lamoureux C G, Lastrapes W D. Forecasting stock-return variance:Toward an understanding of stochastic implied volatilities [J]. Review of Financial Studies, 1993, 6(2):293-326.

[21] Bollerslev T. Generalized autoregressive conditional heteroskeda sticity [J]. Journal of Econometrics, 1986, 31(3):307-327.

[22] Glosten L R, Jagannathan R, Runkle D E.On the relation between the expected value and the volatility of the nominal excess return on stocks[J]. The Journal of Finance, 1993, 48(5):1779-1801.

[23] Christoffersen P F. Elements of financial risk management [M]. Massachusetts:Academic Press, 2002.

[24] Dowd K. Measuring market risk [M]. New Jersey:John Wiley & Sons, Ltd, 2005.

[25] Ding Zhuanxin, Granger C W J, Engle R F. A long memory property of stock market returns and a new model [J]. Journal of Empirical Finance, 1993, 1(1):83-106.

[26] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3):307-327.

[27] Engle R F.Autoregressive conditional heteroskedasticty with estimates of the variance of U. K. inflation [J]. Econometrica, 1982, 50(4):987-1008.

[28] Baillie R T, Bollerslev T, Mikkelsen H O. Fractionally integrated generlized autoregressive conditional heteroscedasticity [J]. Journal of Econometrics, 1996, 74(1):3-30.

[29] Genest C, MacKay J. The Joy of copulas:bivariate distributions with uniform marginals[J]. The American Statistician, 1986, 40(4):280-280.

[30] Hu Ling. Dependence patterns across financial markets:A mixed copula approach[J]. Applied Financial Economics, 2006, 16(10):717-729.

[31] Frahm G, Junker M, Schmidt R. Estimating the tail-dependence coefficient:Properties and pitfalls[J]. Insurance:Mathematics and Economics, 2005, 37(1):80-100.

Study on Dependency Structure of Financial Markets Based on the Stylized Facts and Mixed Copula Function

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