Song, Wenjuan and Sun, Lixin (2014): The Measurement of the Long-Term and Short-Term Risks of Chinese Listed Banks. Published in: Finance Forum , Vol. 2014, No. I (10) (5 October 2014): pp. 37-46.
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Abstract
In this paper, we employ Semi-APARCH model to measure and analyze the long-term and the short-term risk of Chinese 16 listed commercial banks between January 2007 and December 2013, and provide an early warning method for financial regulation by developing a scale function. We find that, first, during the financial crisis of 2008-2009, the long-term risk levels of Chinese banking industry as a whole and the individual commercial banks are very higher, they gradually declined to the normal level only after 2010. Secondly, the current risk of Chinese banks and banking industry is at lower level. Thirdly, the surging of overnight rate in 2013 increased the risk level of commercial banks, which could increase more, of which the regulator should be more cautious. Fourthly, the leverage-effects in the short-term risk of Chinese commercial banks are lower; t-distribution shows a fat-tail. Fifthly, the scale functions of commercial banks are highly correlated, the correlation coefficients are close to 1, which indicates a significantly systematically correlations between the long-term risk of Chinese commercial banks.
Item Type: | MPRA Paper |
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Original Title: | The Measurement of the Long-Term and Short-Term Risks of Chinese Listed Banks |
English Title: | The Measurement of the Long-Term and Short-Term Risks of Chinese Listed Banks |
Language: | Chinese |
Keywords: | Commercial Banks; Long-term Risk; Short-term Risk; Semi-APPARCH Model; Chinese Financial Market |
Subjects: | G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages |
Item ID: | 70007 |
Depositing User: | Dr. Lixin Sun |
Date Deposited: | 23 Mar 2016 07:19 |
Last Modified: | 29 Sep 2019 07:10 |
References: | [1] 高国华,潘英丽.基于动态相关性的我国银行系统性风险度量研究[J].管理评论,2013。 [2] 洪永淼,成思危,刘艳辉等.中国股市与世界其他股市之间的大风险溢出效应.[J].经济学季刊,2004(3) [3] 刘春航,朱元倩.银行业系统性风险度量框架的研究.[J].国际金融研究,2011(12)85-99 [4] 刘晓星,段斌,谢福座.股票市场风险溢出效应研究:基于EVT-Copula-CoVaR模型的分析.[J].世界经济,2011(11),145-159 [5] 穆春舟.中国金融风险评估与风险预警研究.[D],吉林大学,2008 [6] 马君潞,范小云,曹元涛.中国银行间市场双边传染的风险评测及其系统性特征分析.[J]经济研究,2007(1),68-78 [7] 叶五一,陈杰成,缪柏其.基于虚拟变量分位点回归模型的条件VaR估计以及杠杆效应分析.[J].中国管理科学,2010(4),1-7 [8]赵进文,韦文彬.基于MES测度我国银行业系统性风险.[J].金融监管研究,2012(8),28-40 [9]Bali T G, Mo H, Tang Y. The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR. [J]. Journal of Banking & Finance, 2008,32(2):269-282. [10]Bee, Marco and Miorelli, Fabrizio. Dynamic VaR models and the peaks over threshold method for market risk measurement: an empirical investigation during a financial crisis. Elenco dei working paper, 2010. [11]Bellegem, V. S. and von Sachs, R.Forecasting economic time series with unconditional time-varying variance,[J] International Journal of Forecasting 20, 2004, 611-627. [12]Bollerslev, T. A conditionally heteroskedastic time series model for speculative prices and rates of return. [J].Review of Economics & Statistics, 1987, 69(3):542-547. [13]Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. [J]. Econometrics 31, 1986, 307-327. [14]Bollerslev, T., R.F. Engle, and D.B. Nelson, ARCH models, in: R.F. Engle and D. McFadden, eds., Handbook of econometrics, Vol. 4 (Elsevier Science B.V., Amsterdam). 1994 [15] Bougerol, P. and N. Picard, Stationarity of GARCH processes and of some nonnegative time series, Journal of Econometrics 52, 1992, 115-128. [16]Brownlees,C.,and R. Engle,Volatility,Correlation and Tails for Systemic Risk Measurement[R].NYU-Stern,Working Paper,2010. [17]Ding Z, Granger C W J, Engle R E. A long memory property of stock market returns and a new model.[J].Journal of empirical finance,1993(1):83-106 [18]Ding, Z., C.W.J. Granger and R.F. Engle. A long memory property of stock market returns and a new model. [J]. Empirical Finance 1, 1993, 83-106. [19]Engle,R.,Anticipating Correlations: A New Paradigm for Risk Management,[J]Princeton University Press,2009. [20]Engle R, Gonzalez Rivera G. Semiparametric ARCH models [J]. Journal of Business and Economic Statistics, 1991,9(4): 345-359. [21]Feng, Y. Estimating the scale function in a general Semi-GARCH framework under weak moment conditions. [J]Forthcoming preprint, University of Paderborn, 2013. [22]Feng,Y and Sun lixin. A Semi-APARCH approach for comparing long-term and short-term risk in Chinese financial market and in mature financial markets. [R]. Center for International Economics Working Paper.2013. [23]Freixas X. Parigi B.M. and Rochet J.C. Systemic Risk,Interbank Relations, and Liquidity Provision by the Central Bank[J]. Journal of Money,Credit and Banking,2000 (32):611-638 [24]Girardi, G. and A. Tolga. How to Account for Interdependence of Risk in Financial Markets? A Garch Approach to Conditional Value at Risk Estimation. Department of Economics Suolk University working paper,2010. [25]Higgins M.L., Bera A.K., A Class of Nonlinear Arch Models International, Economic Review 33, 1992,137–158. [26]McALeer, M. and da Veiga, B. Spillover effects in forecasting volatility and VaR. School of economics and commerce university of western Australia, 2005. [27]Michael Schroder, Martin Schuler. The Systemic Risk in European Banking Evidence from Bivariate GARCH Models[R]. Center for European Economic Research, Working Paper, 2003 [28]Mikosch T,Starica C. Linit theory for the sample autocorrelations and extremes of a GARCH(1,1) process[J].Annals of statistics ,2000(05):1427-1451. [29]Nelson, D.B., 1990a, Stationarity and persistence in the GARCH(1,1) model, Econometric Theory 6,318-334. [30]Nelson D B. Conditional hteroskedasticity in asset returns: a new approach. [J].Econometric, 1991, 59(2):347-370. [31]Nelson, D.B. and C.Q. Cao, Inequality constraints in the univariate GARCH model, Journal of Business and Economic Statistics 10, 1992, 229-235, [32]Press, W.H. (ed.). Numerical recipes in C. Cambridge Univ. Press, Cambridge. 1996. [33]Sklar A. Functions de repartition an dimensions et leursmarges .[J]. Publications de LInstitutde. Statistiquede L’Universite de Paris, 1959,(8). [34]Wurtz, D., Chalabi, Y. and Luksan, L. Parameter Estimation of ARMA Models with GARCH/APARCH Errors An R and S-Plus Software Implementation. Journal of Statistical Software to appear. 2013. [35]Zakoian J.M., Threshold Heteroskedasticity Models, Journal of Economic Dynamics and Control 15, 1994,931–955. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/70007 |