我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量

中国管理科学 ›› 2014, Vol. 22 ›› Issue (7): 10-17.

我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量

陈守东^1,2, 王妍²

1. 吉林大学数量经济研究中心, 吉林长春 130012;
2. 吉林大学商学院, 吉林长春 130012

收稿日期:2012-12-07 修回日期:2014-02-19 出版日期:2014-07-20 发布日期:2014-07-24
作者简介:陈守东（1955-），男（汉族），天津蓟县人，吉林大学商学院，教授，博士生导师，研究方向：金融计量分析。
基金资助:
教育部人文社科重点研究基地重大项目（2009JJD790015）；国家社科基金项目（12BJY158）

Measuring Systemic Financial Risk of China’s Financial Institution——Applying Extremal Quantile Regression Technology and CoVaR Model

CHEN Shou-dong^1,2, WANG Yan²

1. Center for Quantitative Economics, Jilin University, Changchun 130012, China;
2. School of Business, Jilin University, Changchun 130012, China

Received:2012-12-07 Revised:2014-02-19 Online:2014-07-20 Published:2014-07-24

摘要/Abstract

摘要： 本文将极值理论引入到系统性金融风险度量中，通过极端分位数回归技术估计我国33家上市金融机构对金融系统整体的风险贡献，并识别出我国系统重要性金融机构。研究结果表明，我国金融机构的市场价值总资产收益率呈现明显的非正态分布特征，使用极端分位数回归技术可以更准确的度量尾部的风险联动性；银行类金融机构的系统性风险贡献水平最高且波动变化最大，系统性风险贡献排名前十的金融机构基本为银行类机构；证券类、保险类、信托类金融机构的风险贡献水平相对较低；通过与其他研究的对比发现，考虑到极端情形下的尾部风险联动性时，股份制商业银行对金融系统的风险贡献上升。本文的研究为系统重要性金融机构的宏观审慎监管提供了实证依据。

关键词: 系统性金融风险, CoVaR, 极端分位数回归, 风险度量

Abstract: Based on the extreme theory, a new approach is presented for measuring systemic financial risk. Using extremal quantile regression, 33 listed financial institutions'contributions to the systemic risk of financial system are estimated and systemically important institutions in China' s financial system are recagnjzed. It is found that, the distributions of the growth rate of market valued total assets and the co-movement of the tail risk can be accurately estimated using the extremal quantile regression. The level and variance of the systemic risk contribution in bank sector are the highest. The top ten systemically important financial institutions are almost in bank sector. The levels of systemic contribution in security sector, insurance sector and trust sector are relatively low. Moreover, systemic contributions of joint-stock commercial banks are higher in our study. An empirical tool is provided in this paper for further macro-prudential regulation of systemically important institutions.

Key words: systemic financial risk, CoVaR, extremal quantile regression, risk measure

中图分类号:

F830

陈守东, 王妍. 我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量[J]. 中国管理科学, 2014, 22(7): 10-17.

CHEN Shou-dong, WANG Yan. Measuring Systemic Financial Risk of China’s Financial Institution——Applying Extremal Quantile Regression Technology and CoVaR Model[J]. Chinese Journal of Management Science, 2014, 22(7): 10-17.

参考文献

[1] De Bandt O,Hartmann P. Systemic risk:A survey[R]. ECB working paper,European Central Bank, 2000.

[2] Schwarcz S L. Systemic risk[R]. Studies Paper,Duke University,2008.

[3] IMF. Republic of Latvia:First review and financing assurances review under the stand-by arrangement,requests for waivers of nonobservance of performance criteria,and rephasing of purchases under the arrangement. IMF Country Report, 2009.

[4] Lehar A. Measuring systemic risk:A risk management approach [J]. Journal of Banking and Finance,2005,29(10):2577-2603.

[5] Merton R. On the pricing of corporate debt:The risk structure of interest rates[J]. Journal of Finance, 1974,29(2):449-470.

[6] Avesani R,Pascual A G,Li Jing. A new risk indicator and stress testing tool:A multifactor N^th-to-default CDS basket[R]. IMF Working Paper,2006.

[7] Huang Xin,Zhou Hao,Zhu Haibin. A framework for assessing the systemic risk of major financial institutions[J]. Journal of Banking and Finance,2009,33(11): 2036-2049.

[8] Segoviano M A,Goodhart C. Banking stability measures[R]. Financial Markets Group,Discussion paper,2009.

[9] Adrian T, Brunnermeier M K. CoVaR[R]. Staff Reports,Federal Reserve Bank of New York,2009.

[10] Acharya V,Pedersen L,Philippon T, et al. Measuring systemic risk[R]. Working paper,New York University,2010.

[11] IMF. Guidance to assess the systemic importance of financial institutions markets and instruments:Initial considerations[R]. Background Papers (Basel),2009.

[12] Kritzman M, Li Yuanzhen,Page S, et al. Principal components as a measure of systemic risk[J]. The General of Portfolio Management, 2011,37(4):112-126.

[13] Chan-Lau J,Espinosa M,Sole J. On the use of network analysis to assess systemic financial linkages[R]. IMF Working Paper,2009.

[14] Billio M,Getmansky M,Lo A W, et al. Econometric measures of systemic risk in the finance and insurance sectors[R]. NBER Working Paper,2010.

[15] Alessi L,Detken C. Real time early warning indicators for costly asset price boom/bust cycles:A role for global liquidity[R]. ECB Working Paper,European Central Bank,2009.

[16] Borio C,Drehmann M. Towards an operational framework for financial stability:'fuzzy'measurement and its consequences[R]. BIS Working Papers,Bank for International Settlements,2009.

[17] 宋群英.基于Copula 函数的系统重要性银行的传染性研究[J]. 金融与经济,2011,(10):12-17.

[18] 高国华,潘英丽.银行系统性风险度量─基于动态CoVaR方法的分析[J]. 上海交通大学学报,2011,(12):1753-1759.

[19] 丁庭栋,赵晓慧.不同行业与金融系统的波动溢出效应分析[J]. 统计与决策,2012,(3):162-166.

[20] 贾彦东.金融机构的系统重要性分析─金融网络中的系统风险衡量与成本分担[J]. 金融研究,2011,(10):17-33.

[21] 陈守东,王妍.金融压力指数与工业一致合成指数的动态关联研究[J]. 财经问题研究,2011,(10):39-46.

[22] Chernozhukov V. Extremal quantile regression[J]. Annals of Statistics,2005,33:806-839.

[23] Koenker R,Bassett G. Regression quantiles[J]. Econometrica,1978,46:33-50.

[24] Chernozhukov V. Inference for extremal conditional quantile models,with an application to birthweights. Working Paper,MIT, 2006.

[25] 张强,吴敏.中国系统重要性银行评估:来自2006-2010年中国上市银行的证据[J]. 上海金融,2011,(11):39-42.