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A numerical study on portfolio VaR forecasting based on conditional copula  

Kim, Eun-Young (Department of Statistics, Hankuk University of Foreign Studies)
Lee, Tae-Wook (Department of Statistics, Hankuk University of Foreign Studies)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.6, 2011 , pp. 1065-1074 More about this Journal
Abstract
During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.
Keywords
ARMA-GARCH model; copula; exponentially weighted moving average; financial time series; simple moving average; VaR;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Hong, C. and Kwon, T. (2010). Distribution fitting for the rate of return and value at risk. Journal of the Korean Data & Information Science Society, 21, 219-229.
2 Hwang, C. and Shin, S. (2010). Estimating GARCH models using kernel machine learning. Journal of the Korean Data & Information Science Society, 21, 419-425.
3 Jorion, P. (2006). Value at risk: The new benchmark for managing financial risk, McGraw-Hill, Boston.
4 Kim, S. and Kim, J. (2009). Analysing financial time series data using the GARCH model. Journal of the Korean Data & Information Science Society, 20, 475-483.
5 Lee, T. (2009). Numerical study on Jarque-Bera normality test for innovations of ARMA-GARCH models. Journal of the Korean Data & Information Science Society, 20, 453-458.
6 Lee, T. and Ha, J. (2007). Testing the domestic financial data for the normality of the innovation based on the GARCH(1,1) model. Journal of the Korean Data & Information Science Society, 18, 809-815.
7 Lee, S and Lee, T (2011). Value at risk forecasting based on Gaussian mixture ARMA-GARCH model. Journal of Statistical Computation and Simulation, 81, 1131-1144.   DOI   ScienceOn
8 Palaro, H. P. and Hotta, L. K. (2006). Using conditional copula to estimate Value at Risk. Journal of Data Science, 4, 93-115.
9 김주철, 김우환 (2009). <금융공학 연구노트>, 자유아카데미, 서울.
10 김철중, 윤만하 (2010). <신용위험측정>, 한국금융연수원, 서울.
11 이상진, 김기범 (2008). 단일변량모형과 다변량모형의 포트폴리오 VaR 측정 성과. <증권학회지>, 37, 877-913.
12 Franke, J., Hardle, W. K. and Hafner, C. M. (2008). Statistics of financial markets, Sprinker-Verlag, Berlin.
13 Christoffersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 39, 841-861.   DOI   ScienceOn