不同風險值模型預測的準確性之評估

Assessing the Predicting Accuracy of Different Value-at-Risk Model

倪于清(Yu-Chin Ni)

指導教授 ： 郭震坤

國立臺灣大學/管理學院/國際企業學研究所/碩士(2006年)

https://doi.org/10.6342/NTU.2006.01308

摘要

近年來我國金融業在積極追求最大獲利之外，對於風險管理的課題也極為重視。而在風險管理系統的建構中，準確的評估風險值或極端風險值為重要的工作之一。本研究的目的為探討不同模型計算風險值和極端風險值的方法，經由風險測度信賴區間的建立，評估不同模型對於事前估計的誤差程度。本文應用GARCH建立動態變異數模型，並且分別對常態分配和t分配計算Hill、FHS、GCCF三種估計值，再用拔靴法來評估風險值和極端風險值預測的準確性。由模擬資料分析可知，FHS和Hill對於風險值和極端風險值的預測準確性較高，此結果在實務上應有相當參考價值。

關鍵字

極端風險值 ； GARCH ； 拔靴法

並列摘要

Recently, the financial industry in Taiwan increasingly uses Value-at-Risk (VaR) in portfolio risk management, risk capital allocation and performance attribution. Risk managers are rightfully concerned with the precision of VaR and the related expected shortfall (ES) techniques. The purposes of this thesis are to estimate the predicating accuracy of VaR and ES computed by different models, and to assess the ex ante magnitude of the error through the construction of confidence intervals around the VaR and ES measures. We apply GARCH to build dynamic variation models, and use normal and t distributions to construct the Hill, FHS, and GCCF estimators. Then, the bootstrap method is used to assess the predicating accuracy of VaR and ES. Finally, by analyzing simulated data, we conclude the FHS and Hill estimators have the higher predicating accuracy in estimating VaR and ES. The result should be useful in practice.

並列關鍵字

Expected shortfall ； GARCH ； Bootstrap method

參考文獻

Andreev, A., and Kanto, A.(2005) “Conditional value-at-risk estimation using non-integer values of degrees of freedom in Student’s t-distribution” The Journal of Risk 7, No 2, pp.55-61.

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Barone-Adesi, G., Giannopulos, K., and Vosper, L.(1999) “VaR without correlations for portfolios of derivative securities” Journal of Futures Markets 19, No. 5, pp.583-602.

Barone-Adesi, G., Giannopulos, K., and Vosper, L.(2000) “Non-parametric VaR techniques: Myths and realities” Working Paper, University of Westminster, November.

Bollerslec, T.(1986) “Generalized autoRegressive conditional heteroskedasticity” Journal of Econometrics 31. pp.307-327.