Browse > Article
http://dx.doi.org/10.5351/CKSS.2009.16.5.791

Combination of Value-at-Risk Models with Support Vector Machine  

Kim, Yong-Tae (Department of Statistics, Dankook University)
Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu)
Lee, Jang-Taek (Department of Statistics, Dankook University)
Hwang, Chang-Ha (Department of Statistics, Dankook University)
Publication Information
Communications for Statistical Applications and Methods / v.16, no.5, 2009 , pp. 791-801 More about this Journal
Abstract
Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.
Keywords
GARCH; historical simulation; model selection; quantile regression; support vector machine(SVM); Value-at-Risk;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Mercer, J. (1909). Functions of positive and negative type, and their connection with the theory of integral equations, Philosophical Transactions of the Royal Society of London. Series A, 299, 415-446   DOI   ScienceOn
2 Nychka, D., Gray, G., Haaland, P., Martin, D. and O’Connell, M. (1995). A nonparametric regression approach to syringe grading for quality improvement, Journal of the American Statistical Association, 90, 1171-1178   DOI   ScienceOn
3 Ozun, A. and Cifter, A. (2007). Nonlinear combination of financial forecast with genetic algorithm, MPRA Paper, No.2488
4 Palit, A. K. and Popovic, D. (2000). Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neuro-fuzzy approach, FUZZ-IEEE, 2, 566-571   DOI
5 Seok, K., Hwang, C. and Cho, D. (1999). Kernel adatron algorithm for support vector regression, Commu-nications of the Korean Statistical Society, 6, 843-847   과학기술학회마을
6 Shim, J., Hwang, C. and Hong, D. H. (2009). Fuzzy semiparametric support vector regression for seasonal time series analysis, Communications of the Korean Statistical Society, 16, 335-348   과학기술학회마을   DOI   ScienceOn
7 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327   DOI   ScienceOn
8 Vapnik, V. N. (1998). Statistical Learning Theory, Springer
9 Yuan, M. (2006). GACV for quantile smoothing splines, Computational Statistics & Data Analysis, 50, 813-829   DOI   ScienceOn
10 Smola, A. J. and Scholkopf, B. (1998). A tutorial on support vector regression, NeuroCOLT2 Technical Report, NeuroCOLT
11 Gunn, S. (1998). Support vector machines for classification and regression, ISIS Technical Report, Uni-versity of Southampton
12 Hwang, C. and Shim, J. (2005). A simple quantile regression via support vector machine, Lecture Notes in Computer Science, 3610, 512-520   DOI   ScienceOn
13 Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk, McGraw-Hill, New York
14 Koenker, R. and Bassett, G. (1978). Regression quantiles, Econometrica, 46, 33-50   DOI   ScienceOn