基于混合分布的中国股票波动风险因素的识别与分析

doi:10.16381/j.cnki.issn1003-207x.2018.02.010

Abstract

Abstract: GARCH models have been widely used in modeling financial time series that exhibit time-varying volatility clustering. In this study, the model-based clustering approach is employed to examine clusters of 1165 stocks on Chinese security market on the basis of the estimated GARCH model parameters. It is found that the 1165 stocks could be divided into 4 clusters:cluster 1 consists of stocks with abnormal volatility features, while for stocks in the other three clusters. The distributions of the GARCH parameters have a similar shape but with different values.Stocks of manufacturing companies and decentralized non-state-owned companies are more likely in the cluster with low volatility.Stocks of public utility companies (electricity, gas, water supply)and real-estate companies are more likely in the cluster with high volatility.

Key words: mixture distribution, GARCH model, stock volatility

CLC Number:

F124

WANG An-xing, TAN Xian-ming. The Analysis of Chinese Stock Volatile Risk Factors based on Mixture Distribution[J]. Chinese Journal of Management Science, 2018, 26(2): 86-95.

References

[1] Fama E F. French K R, Dissecting anomalies with a five-factor model.Review of Financial Studies, 2016, 29(1):69-103.

[2] Fama E F. French K R. A five-factor asset pricing model.Journal of Financial Economics,2015,116(1):1-22.

[3] Jegadeesh N,Titman S. Returns to buying winners and selling losers:Implications for stock market efficiency.Journal of Finance,1993, 48, 65-91.

[4] Engle R F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 1982, 50(4):987-1007.

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

[6] Bollerslev T. A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics and Statistics,1987, 69(3), 542-547.

[7] Lamoureux C G, Lastrapes W D. Heteroskedasticity in stock return data:Volume versus GARCH effects. Journal of Finance, 1990, 41(1):221-229.

[8] Bessembinder H, Seguin P J. Price volatility, trading volume, and market depth:Evidence from futures markets. Journal of Financial and Quantitative Analysis, 1993, 28(1):21-39.

[9] 赵军力, 梁循. 基于TrTS取样的股票收益率RV测度的改进. 中国管理科学, 2015, 23(7):26-34.

[10] 刘祥东, 范彬, 杨易铭,等. 基于M-Copula-SV-t模型的高维组合风险度量. 中国管理科学, 2017, 25(2):1-9.

[11] Fraley C, Raftery A E. Model-based clustering, discriminant analysis and density estimation[J]. Journal of the American Statistical Association.2002, 97(458):611-631.

[12] Dempster A P, Laird N M, Rubin D B.Maximum-likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society,1997, 39(1):1-38.

[13] Schlattmann P. Medical applications of finite mixture models[M]. Springer Series:Statistics for Biology and Health,2009.

[14] Thomas E M, Temko A, Lightbody G, et al, Gaussian mixture models for classification of neonatal seizures using EEG.Physiological Measurement,2010, 31(7):1047-64.

[15] 刘艳琪,胡亨伍.基于EM算法的混合模型医学图像分割.计算机工程,2012, 38(2):231-233.

[16] Tauchen G E, Pitts M. The price variability-volume relationship on speculative markets. Econometrica, 1983, 51(2):485-505.

[17] Andersen T G. Return volatility and trading volume:An information flow interpretationof stochastic volatility. Journal of Finance,1996, 51(1):169-204.

[18] Lamoureux C G, Lastrapes W D. Endogenous trading volume and momentum in stock-return volatility. Journal of Business & Economic Statistics, 1994, 12(2):253-260.

[19] Liesenfeld R. Dynamic bivariate mixture models:Modeling the behavior of prices and trading volume. Journal of Business & Economic Statistics,1998, 16(1):101-109.

[20] Mitchell M L, Mulherin J H. The impact of public information on the stock market. Journal of Finance, 1994, 49(3):923-950.

[21] Berry T D, Howe K M. Public information arrival. Journal of Finance,1994, 49(4), 1331-1346.

[22] Liesenfeld R. A generalized bivariate mixture model for stock price volatility and trading volume. Journal of Econometrics,2001, 104(1):141-178.

[23] 杨瑞成,秦学志,陈田,等,基于混合分布单因子模型的CDO定价问题,数理统计与管理,2009, 28(6),1082-1090.

[24] Fraley C. Raftery A E. Murphy T B,et al, Mclust Version 4 for R:Normal mixture modeling for model-based clustering, classification, and density estimation technical[R]. Report No. 597,University of Washington.

[25] Thomas E M,Femko A,Lightbody G,et al. Gaussian mixture models for classification of neonatal seizures using EEG[J]. Physiological Measurement, 2010,31(7):1047-1064.

The Analysis of Chinese Stock Volatile Risk Factors based on Mixture Distribution

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