基于贝叶斯极端分位数回归的金融风险相依性研究
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- 英文论文题名：Finanical Risk Interdependence Based on Baysian Extremal Quantile Regression
- 论文作者：朱慧明 ; 王春晗 ; 任英华 ; 彭成
- 英文论文作者：ZHU Hui-ming ; WANG Chun-han ; REN Ying-hua ; PENG Cheng ; College of business Administration ; Hunan University
- 年：2016
- 作者机构：湖南大学工商管理学院;
- 论文关键词：风险相依性 ; 联动VaR ; 分位数回归 ; 贝叶斯分析 ; MCMC算法 ; 状忑变量
- 英文论文关键词：risk interdependence ; conditional value at risk ; quantile regression ; Bayesion analysis ; MCMC algorithm ; state variables
- 会议召开时间：2016-11-12
- 会议录名称：第十八届中国管理科学学术年会论文集
- 英文会议录名称：Proceedings of the 18th Chinese Annual Conference on Management Science
- 语种：中文
- 分类号：F224;F831
- 学会代码：ZHYJ
- 会议名称：第十八届中国管理科学学术年会
- 会议地点：中国陕西西安
- 主办单位：中国优选法统筹法与经济数学研究会、西安交通大学、中国科学院科技战略咨询研究院、《中国管理科学》编辑部
- 学会名称：中国优选法统筹法与经济数学研究会
- 页数：9
- 文件大小：2329k
- 原文格式：O
- 会议级别：全国
摘要
针对分位回归模型参数的不确定性问题,构建基于贝叶斯分位数回归的联动风险价值模型,据此研究金融风险的相依性问题。通过模型的统计结构分析,选择参数先验分布,设计贝叶斯MCMC算法估计参数。并利用机构总资产收益率进行实证分析。研究结果表明:贝叶斯分位回归模型可以有效地描绘极端风险下的相依性,金融业的风险相依性大于实体行业。
In order to research financial risk interdenpdence,a conditional value at risk model based on Bayesian quantile regression for the uncertainty risks of quantile regression model parameters is constructed.Based on analysis of statistic of model and the selection of parameters prior,the Bayesian MCMC is utilized to estimate model parameters.The empricial research applies the growth rate of institutions' market valued total asset.The research results show that the interdependece of the risk can be accurately estimated using bayesian quantile regression.The interdependece of the risk in the financial sector is stronger than that in the real economy.
In order to research financial risk interdenpdence,a conditional value at risk model based on Bayesian quantile regression for the uncertainty risks of quantile regression model parameters is constructed.Based on analysis of statistic of model and the selection of parameters prior,the Bayesian MCMC is utilized to estimate model parameters.The empricial research applies the growth rate of institutions' market valued total asset.The research results show that the interdependece of the risk can be accurately estimated using bayesian quantile regression.The interdependece of the risk in the financial sector is stronger than that in the real economy.
引文
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[16]Kobayashi H.Gibbs sampling methods for Bayesian quantile regression[J].Journal of Statistical Computation&Simulation,2009,81(11):1565-1578.
[17]Dyk DAV,Jiao Xiyan.Metropolis-Hastings Within Partially Collapsed Gibbs Samplers[J].Journal of Computational and Graphical Statistics,2015,24(2):301-327.
[18]Dyk DAV,Park T.Partially Collapsed Gibbs Samplers[J].Journal of the American Statistical Association,2008,103:790-796.
[19]Bernardi M,Gayraud G,Petrella L,et al.Bayesian tail risk interdependence using quantile regression[J].Bayesian Analysis,2015,10(3):553-603.
[2]Nier E,Yang J,Yorulmazer T,et al.Network models and financial stability[J].Bank of England Quarterly Bulletin,2008,31(6):2033-2060.
[3]May R,Arinaminpathy N.Systemic risk:the dynamics of model banking systems.[J],Journal of the Royal Society Interface,2009,46(7):823-38.
[4]Zhu Xiaoqian,Xie Yongjia,Li Jianping.Change point detection for subprime crisis in American banking:From the perspective of risk dependence[J].International Review of Economics&Finance,2015,38(3):18-28.
[5]Billio M,Getmansky M,Lo A W,et al.Econometric measures of connectedness and systemic risk in the finance and insurance sectors[J].Journal of Financial Economics,2011,104(3):535-559.
[6]苟文均,袁鹰,漆鑫.债务杠杆与系统性风险传染机制——基于CCA模型的分析[J].金融研究,2016,(3):74-91.
[7]欧阳红兵,刘晓东.中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J].中国管理科学,2015,23(10):30-37.
[8]Adrian T,Brunnermeier M.CoVaR[J].American Economic Review,2016,106(7):1705-1742.
[9]Lee C,Su J.Alternative statistical distributions for estimating value-at-risk:Theory and evidence[J].Review of Quantitative Finance&Accounting,2011,39(3):309-331.
[10]Bernardi M,Maruotti A,Petrella L.Skew mixture models for loss distributions:A Bayesian approach[J].Insurance Mathematics&Economics,2012,51(3):617-623.
[11]Bernardi M,Catania L.Comparison of Value-atRisk models using the MCS approach[J].Computational Statistics,2016,31(2):579-608.
[12]Girardi G,Erg(u|¨)n A T.Systemic risk measurement;Multivariate GARCH estimation of CoVaR[J].Journal of Banking&Finance,2013,37(8):3169-3180.
[13]Castro C,Ferrari S.Measuring and testing for the systemically important financial institutions[J].Journal of Empirical Finance,2012,25(3):1-14.
[14]Tsionas A.Bayesian quantile inference[J].Journal of Statistical Computation&Simulation,2003,73(9):659-674.
[15]Yu K,Moyeed R.Bayesian quantile regression[J].Ssrn Electronic Journal,2001,25(6):287-307.
[16]Kobayashi H.Gibbs sampling methods for Bayesian quantile regression[J].Journal of Statistical Computation&Simulation,2009,81(11):1565-1578.
[17]Dyk DAV,Jiao Xiyan.Metropolis-Hastings Within Partially Collapsed Gibbs Samplers[J].Journal of Computational and Graphical Statistics,2015,24(2):301-327.
[18]Dyk DAV,Park T.Partially Collapsed Gibbs Samplers[J].Journal of the American Statistical Association,2008,103:790-796.
[19]Bernardi M,Gayraud G,Petrella L,et al.Bayesian tail risk interdependence using quantile regression[J].Bayesian Analysis,2015,10(3):553-603.