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Vector at Risk and alternative Value at Risk
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 Title & Authors
Vector at Risk and alternative Value at Risk
Honga, C.S.; Han, S.J.; Lee, G.P.;
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The most useful method for financial market risk management may be Value at Risk (VaR) which estimates the maximum loss amount statistically. The VaR is used as a risk measure for one industry. Many real cases estimate VaRs for many industries or nationwide industries; consequently, it is necessary to estimate the VaR for multivariate distributions when a specific portfolio is established. In this paper, the multivariate quantile vector is proposed to estimate VaR for multivariate distribution, and the Vector at Risk for multivariate space is defined based on the quantile vector. When a weight vector for a specific portfolio is given, one point among Vector at Risk could be found as the best VaR which is called as an alternative VaR. The alternative VaR proposed in this work is compared with the VaR of Morgan with bivariate and trivariate examples; in addition, some properties of the alternative VaR are also explored.
 Cited by
다변량 정규분포에서 대안적인 VaR의 특성,홍종선;이기쁨;

Journal of the Korean Data and Information Science Society, 2016. vol.27. 6, pp.1453-1463 crossref(new window)
다변량 조건부 꼬리 기대값,홍종선;김태우;

응용통계연구, 2016. vol.29. 7, pp.1201-1212 crossref(new window)
Properties of alternative VaR for multivariate normal distributions, Journal of the Korean Data and Information Science Society, 2016, 27, 6, 1453  crossref(new windwow)
Andersson, F., Mausser, H., Rosen, D., and Uryasev, S. (2001). Credit risk optimization with condition value-at-risk, Mathematical Programming, 89, 273-291. crossref(new window)

Barone-Adesi, G., Giannopoulos, K., and Vosper, L. (1999). VaR without correlations for portfolio of derivative securities, Journal of Futures Markets, 19, 583-602. crossref(new window)

Berkowitz, J., Christoffersen, P., and Pelletier, D. (2011). Evaluating Value-at-Risk models with desk-level data, Management Science, 57, 2213-2227. crossref(new window)

Chen, L. A. and Welsh, A. H. (2002). Distribution-function-based bivariate quantiles, Journal of Multivariate Analysis, 83, 208-231. crossref(new window)

Heo, S. J., Yeo, S. C., and Kang, T. H. (2012). Performance analysis of economic VaR estimation using risk neutral probability distributions, Korean Journal of Applied Statistics, 25, 757-773. crossref(new window)

Hong, C. S. and Kwon, T. W. (2010). Distribution fitting for the rate of return and value at risk, Journal of the Korean Data & Information Science Society, 21, 219-229.

Hong, C. S. and Lee, J. H. (2011a). VaR estimation of multivariate distribution using Copula functions, Korean Journal of Applied Statistics, 24, 523-533. crossref(new window)

Hong, C. S. and Lee, W. Y. (2011b). VaR estimation with multiple Copula functions, Korean Journal of Applied Statistics, 24, 809-820. crossref(new window)

Jorion, P. (2007). Value at Risk, The New Benchmark for Market Risk (1st Ed.), McGraw-Hill, New York.

Kang, M. J., Kim, J. Y., Song, J. W., and Song, S. J. (2013). Value at Risk with peaks over threshold: comparison study of parameter estimation, Korean Journal of Applied Statistics, 26, 483-494. crossref(new window)

Krokhmal, P., Palmquist, J., and Uryasev, S. (2002). Portfolio optimization with conditional Value-at-Risk objective and constraints, Journal of Risk, 4, 11-27.

Kupiec, P. (1995). Techniques for verifying the accuracy of risk management models, Journal of Derivatives, 2, 73-84. crossref(new window)

Li, D. X. (1999). Value at Risk based on the volatility skewness and kurtosis, Available from:, RiskMetrics Group.

Longin, F. M. (2000). From value at risk to stress testing: the extreme value approach, Journal of Banking & Finance, 24, 1097-1130. crossref(new window)

Longin F. M. (2001). Beyond the VaR, Journal of Derivatives, 8, 36-48. crossref(new window)

Lopez, J. A. (1998). Methods for evaluating Value-at-Risk estimates, Economic Policy Review, 4, 119-124.

Morgan, J. P. (1996). RiskMetrics, Technical Document (4th Ed.), JP Morgan, New York.

Neftci, S. N. (2000). Value-at-Risk calculation extreme events and tail estimation, Journal of Derivatives, 7, 23-37. crossref(new window)

Park, J. S. and Jung, M. S. (2002). Market risk management strategies through VaR, KISDI Research Papers, Fall 2002, KISDI.

Park, K. H., Ko, K. Y., and Beak, J. S. (2013). An one-factor VaR model for stock portfolio, Korean Journal of Applied Statistics, 26, 471-481 crossref(new window)

Rockafellar, R. T. and Uryasev, S. (2000). Optimization of conditional value-at-risk, Journal of Risk, 2, 21-41. crossref(new window)

Rockafellar, R. T. and Uryasev, S. (2002). Conditional value-at-risk for general loss distributions, Journal of Banking & Finance, 26, 1443-1471. crossref(new window)

Seo, S. H. and Kim, S. G. (2010). Estimation of VaR using extreme losses, and back-testing: case study, Korean Journal of Applied Statistics, 23, 219-234. crossref(new window)

Yeo, S. C. and Li, Z. (2015). Performance analysis of volatility models for estimating portfolio value at risk, Korean Journal of Applied Statistics, 28, 541-599. crossref(new window)

Yuzhi, C. (2010). Multivariate quantile function models, Statistica Sinica, 20, 481-496.

Zangari, P. (1996). An improved methodology for measuring VaR, RiskMetrics Monitor, 2, 7-25.