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Volume 4, Issue 3, June 2015, Page: 116-121
Study on Dynamic Risk Measurement Based on ARMA-GJR-AL Model
Hong Zhang, School of Information, Beijing Wuzi University, Beijing, China
Li Zhou, School of Information, Beijing Wuzi University, Beijing, China
Jian Guo, School of Information, Beijing Wuzi University, Beijing, China
Received: Mar. 30, 2015;       Accepted: Apr. 16, 2015;       Published: Apr. 27, 2015
DOI: 10.11648/j.acm.20150403.13      View  3397      Downloads  157
Abstract
This paper established the ARMA-GJR-AL model of dynamic risk VaR and CVaR measurement. Considering from aspects of the correlation and volatility and residual distribution characteristics, studying the dynamic risk measures of VaR and CVaR based on ARMA-GJR-AL model. Through empirical research, Risk prediction and accuracy of inspection are given of the Shanghai stock market and the New York stock market. And we study the effectiveness of the model. The results show that the dynamic risk measurement model based on AL distribution is more reasonable and applicability, so it can effectively measure risk.
Keywords
ARMA-GJR-AL Model, VaR, Financial Market Risk
To cite this article
Hong Zhang, Li Zhou, Jian Guo, Study on Dynamic Risk Measurement Based on ARMA-GJR-AL Model, Applied and Computational Mathematics. Vol. 4, No. 3, 2015, pp. 116-121. doi: 10.11648/j.acm.20150403.13
Reference
[1]
Black F. The Dividend Puzzle [J]. Journal of Portfolio Management, 1976, 2 (2) 6-7.
[2]
Black F., Scholes M. The pricing of options and corporate liabilities [J]. Journal of Political Economy, 1973, 81 (3): 639-657.
[3]
Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31: 317-324.
[4]
Bollerslev T. Generalized autoregressive conditional heteroskedasticity [J]. Journal of Econometrics, 1986, 31 (3): 309-317.
[5]
Bollerslev T. Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Mode [J]. Review of Economics and Statistics,1990, 72: 499-503.
[6]
Bollerslev T., Engle R.F., Wooldridge M.J. A capital Asset Pricing Model with time-varying covariances [J]. Journal of Political Economy, 1988, 96: 119-130.
[7]
Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation [J]. Econometric, 1982, 50 (4): 989-1004.
[8]
Engle R.F., Kroner F.K. Multivariate Simultaneous Generalized ARCH [J].Econometric Theory, 1995, 11: 135-149.
[9]
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: 395-406.
[10]
Engle Robert F. Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models [J]. Journal of Business and Economic Statistics, 2002, 20 (3):341-347.
[11]
Glosten L. R., Jagannathan R. and Runkle D. E. On the relation between expected value and the volatility of the nominal excess return on stocks [J]. The Journal of Finance, 1993, 48 (5): 1779-1801.
[12]
Nelsen R.B. An introduction to Copulas [M]. New York: Springer-Verlag, 1999.
[13]
Nelson B. Conditional heteroscedasticity in asset returns: a new approach [J]. Econometrica, 1991, 59: 349-360.
[14]
Nelson D.B. ARCH models as diffusion approximations [J]. Journal of Econometrics, 1990, 45: 9-28.
[15]
Zakoian J.M. Threshold heteroskedastic models [J]. Journal of Economic Dynamics and Control, 1990, 18: 937-945.
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