Measurement of Dynamic Portfolio VaR Based on Mixed Vine Copula Model
Journal of Finance and Accounting
Volume 5, Issue 2, March 2017, Pages: 80-86
Received: Apr. 21, 2017; Published: Apr. 21, 2017
Views 1937      Downloads 154
Authors
Zhao Ru-bo, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Tian Yi-xiang, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Tian Wei, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Chen Xiu-rong, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
Article Tools
Follow on us
Abstract
The measurement of portfolio VaR has been a hot issue in the field of the academic and the industry. This paper applies three kinds of Vine Copula model to describe high-dimensional dependency structure between multiple assets, introduces mixed binary copula function to improve the accuracy of tail dependence structure. We use six important stock markets as stock portfolio to test this model. The empirical results show that introducing mixed Copula function can improve the measurement reliability of Vine Copula model, and the reliability of mixed R-Vine model is highest in three kinds of mixed Vine Copula models.
Keywords
Mixed Copula, Vine Copula, Dynamic of VaR, Portfolio
To cite this article
Zhao Ru-bo, Tian Yi-xiang, Tian Wei, Chen Xiu-rong, Measurement of Dynamic Portfolio VaR Based on Mixed Vine Copula Model, Journal of Finance and Accounting. Vol. 5, No. 2, 2017, pp. 80-86. doi: 10.11648/j.jfa.20170502.12
References
[1]
Lin Yu, “Measuring Dynamic Risk of Financial Markets Based on Stylized Facts and Extreme Value Theory,” Investment Research, vol. 31, pp. 41-56, 2012.
[2]
Wang peng, “Calculating VaR and ES based on volatility models with time-varying higher-moments,” Journal of Management Sciences in China, vol.16, pp.33-45, 2013.
[3]
J. L. Wu, G. Chen, and C. Huang, “Long-term dynamic trends in tail dependence of Chinese A, B and H stock markets: Empirical analysis based on multi-regime smoothing transition mixed copula model,” Journal of Management Sciences in China, vol. 18, pp. 50-65, 2015.
[4]
J. L. Wu and E. H. Zhang, “Subprime mortgage crisis, market risk and stock market interdependence,” World Economic, vol. 3, pp.95-108, 2010.
[5]
W. W. Guo and M. Zhong, “An empirical study on dependency structure and risk measure of style portfolio in Chinese stock market based on vine copula model,” Manage Review, vol.25, pp.41-52, 2013.
[6]
H. Joe, “Families of m-variate distributions with given margins and m (m-1)/2 bivariate dependence parameters,” Lecture Notes-Monograph Series, pp.120-141, 1996.
[7]
H. Joe, “Multivariate models and multivariate dependence concepts,” CRC Press, 1997.
[8]
T. Bedford and R. M. Cooke, “Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines,” Annals of Mathematics and Artificial Intelligence, vol. 32, pp. 245-268, 2001.
[9]
T. Bedford and R. M. Cooke, “Vines: A New Graphical Model for Dependent Random Variables,” Annals of Statistics, vol. 30, pp. 1031-1068, 2002.
[10]
Y. H. Wei and S. T. Qi, “A Study on the Crisis of Financial Crisis in Emerging Markets in Asia - A Method Based on Copula 's Theory,” International Finance Research, vol.9, pp. 22-29, 2008.
[11]
R. Aloui, M. S. B. Aïssa and D. K. Nguyen, “Global financial crisis, extreme interdependences, and contagion effects: The role of economic structure?” Journal of Banking & Finance, vol.35, pp.130-141, 2010.
[12]
L. Deng, C. Ma and W. Yang, “Portfolio optimization via pair copula-GARCH-EVT-CVaR model,” Systems Engineering Procedia, vol.2, pp. 171-181, 2011.
[13]
X. H. Zhou, B. S. Zhang and Y. W. Dong, “Risk measurement of financial portfolio based on Copula-SV-GPD model,” Journal of Management Sciences in China, vol. 15, pp.70-78, 2012.
[14]
E. C. Brechmann and C. Czado, “Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50,” Statistics & Risk Modeling, vol.30, pp.307-342, 2013.
[15]
G. N. F. Weiß, “Supper H. Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas,” Journal of Banking & Finance, vol.37, pp. 3334-3350, 2013.
[16]
G. B. Fan, Y. Zeng and W. G. Huang, “A multi - asset portfolio risk measure the way: just rattan copula,” Quantitative Economic, Technical and Economic Research, vol.1, pp.88-102, 2013.
[17]
J. Gao, “Vine Copula model an VaR forecast for multi-asset portfolio,” Journal of Applied Statistics and Management, vol.32, pp.247-258, 2013.
[18]
B. Z. Zhang, Y. Wei and J. Yu, “The study of correlation and portfolio selection among multi-markets based on EVT-Vine-Copula,” Journal of Management Science, vol.05, pp.133-144, 2014.
[19]
F. Ma, Y. Wei and D. S. Huang, “Measurement of dynamic stocks portfolio VaR and its forecasting model based on vine copula,” Systems Engineering-Theory & Practice, vol. 35, pp.26-36, 2015.
[20]
L.T. Zhao, T. Li and Y. J. Zhang, “Measuring the price risk of energy portfolio with copula-VaR model,” System Engineering-Theory & Practice, vol. 35, pp.771-779, 2015.
[21]
A. Sklar,”“Fonctionde repartition a dimension stleurs marges,” Publ Inst Stat Univ Paris, 1959, pp.229-231.
[22]
E. C. Brechmann, “Truncated and simplified regular vines and their applications,” Diploma Thesis: Technische Universitat München, 2010.
[23]
P. F. Christoffersen, “Evaluating interval forecasts,” International economic review, pp.841-862, 1998.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186