互联网金融波动性的MCMC算法分析
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摘要
基于金融市场的波动聚集、杠杆效应等种种特性,建立ARMA-GJR-GARCH模型并选取互联网金融板块日对数收益率的数据对互联网金融股市的波动性进行分析,采用MCMC方法对模型的参数进行贝叶斯估计以充分利用先验信息和样本信息使参数估计更精确并解决高维数值积分的不便.结果表明:互联网金融板块收益率存在比较明显的杠杆效应,利空消息比利好消息对互联网金融市场产生的冲击更大,且在互联网金融市场中,突发事所引起的风险可以被有效控制.
Based on the characteristics of volatility aggregation and leverage effect in financial market, ARMA-GJR-GARCH model was istablished witch selects the data of daily logarithmic yield of internet financial sector to analyze the volatility of internet financial stock market volatility. The MCMC method performs Bayesian estimation on the parameters of the model to make full use of the prior information and sample information to make the parameter estimation more accurate and solve the inconvenience of high-dimensional numerical integration. The research shows that there is a significant leverage effect on the profitability of the internet financial sector. The bad news has a greater impact on the internet financial market than the good news, and the risks caused by unexpected events in the internet financial market can be effectively controlled.
Based on the characteristics of volatility aggregation and leverage effect in financial market, ARMA-GJR-GARCH model was istablished witch selects the data of daily logarithmic yield of internet financial sector to analyze the volatility of internet financial stock market volatility. The MCMC method performs Bayesian estimation on the parameters of the model to make full use of the prior information and sample information to make the parameter estimation more accurate and solve the inconvenience of high-dimensional numerical integration. The research shows that there is a significant leverage effect on the profitability of the internet financial sector. The bad news has a greater impact on the internet financial market than the good news, and the risks caused by unexpected events in the internet financial market can be effectively controlled.
引文
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[3] ENGLE R F, LILIEN D M, ROBINS R P. Estimating time varying risk premia in the term structure:The Arch-M model[J]. Econometrica, 1987, 55(2):391-407.
[4] 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]. Journal of Finance,1993, 48(5):1779-1801.
[5]赵伟,梁循.互联网金融信息量与收益率波动关联研究[J].计算机技术与发展,2009,19(12):1-4.
[6]谢平,邹传伟,刘海二.互联网金融监管的必要性与核心原则[J].国际金融研究,2014(8):3-9.
[7]张昭,李安渝.互联网金融市场价格波动研究:基于GARCH族模型的实证[J].发展改革理论与实践,2014(10):35-38.
[8]陈倩文.基于GRACH-VAR模型的互联网理财产品市场风险管理研究[J].价值工程,2015,34(20):178-180.
[9]陈霄,叶德珠.中国P2P网络借贷利率波动研究[J].国际金融研究,2016,345(1):83-96.
[10] CHIB S, GREENBERG E. Bayes inference in regression models with ARMA(p, q)errors[J]. Journal of Econometrics,1994, 64(1-2):183-206.
[11] TIAO G C, BOX G E P. Some comments on“Bayes”estimators[J]. American Statistician, 1973, 27(1):12-14.
[12] NAKATSUMA T. Bayesian analysis of ARMA–GARCH models:A Markov chain sampling approach[J]. Journal of Econometrics, 2000, 95(1):57-69.
[13] HASTINGS W K. Monte Carlo sampling methods using Markov chains and their applications[J]. Biometrika, 1970, 57(1):97-109.
[14] GEMAN S, GEMAN D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984(6):721-741.
[15] TANNER M A. Tools for Statistical Inference:Methods for the Exploration of Posterior Distributions and Likelihood Functions[M]. 3rd ed. New York:Springer-Verlag, 1996.