Value at risk estimation by quantile regression and kernel estimator

被引：5
作者：
Huang A.Y. [1 ]
机构：
[1] College of Management, Yuan Ze University, Chung-Li, Taoyuan, 32003
来源：
Review of Quantitative Finance and Accounting | 2013年 / 41卷 / 2期
关键词：
Kernel estimator; Quantile regression; Value at risk;
D O I：
10.1007/s11156-012-0308-x
中图分类号：
学科分类号：
摘要：
Risk management has attracted a great deal of attention, and Value at Risk (VaR) has emerged as a particularly popular and important measure for detecting the market risk of financial assets. The quantile regression method can generate VaR estimates without distributional assumptions; however, empirical evidence has shown the approach to be ineffective at evaluating the real level of downside risk in out-of-sample examination. This paper proposes a process in VaR estimation with methods of quantile regression and kernel estimator which applies the nonparametric technique with extreme quantile forecasts to realize a tail distribution and locate the VaR estimates. Empirical application of worldwide stock indices with 29 years of data is conducted and confirms the proposed approach outperforms others and provides highly reliable estimates. © 2012 Springer Science+Business Media, LLC.
引用
收藏
页码：225 / 251
页数：26
相关论文
共 48 条
  • [1] Angelidis T., Benos A., Degiannakis S., A robust VaR model under different time periods and weighting schemes, Rev Quant Finan Acc, 28, pp. 187-201, (2007)
  • [2] Baillie R.T., Bollerslev T., Mikkelsen M.O., Fractionally integrated generalized autoregressive conditional heteroskedasticity, J Econom, 74, pp. 3-30, (1996)
  • [3] Baixauli J.S., Alvarez S., Evaluating effects if excess kurtosis on VaR estimates: evidence for international stock indices, Rev Quant Finan Acc, 27, pp. 27-46, (2006)
  • [4] Bali T.G., Mo H., Tang Y., The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR, J Bank Finance, 32, pp. 269-282, (2008)
  • [5] Bollerslev T., Generalized autoregressive conditional heteroscedasticity, J Econom, 31, pp. 307-327, (1986)
  • [6] Brooks C., Burke S.P., Persand G., Autoregressive conditional kurtosis, J Financ Econ, 3, pp. 399-421, (2005)
  • [7] Butler J.S., Schachter B., Estimating value-at-risk with a precision measure by combining kernel estimation with historical simulation, Rev Deriv Res, 1, pp. 371-390, (1997)
  • [8] Cai Z., Wang X., Nonparametric estimation of conditional VaR and expected shortfall, J Econom, 147, pp. 120-130, (2008)
  • [9] Chen H., Value-at-Risk efficient portfolio selection using goal programming, Rev Pacific Basin Financ Markets Policies, 11, 2, pp. 187-200, (2008)
  • [10] Chen F.Y., Liao S.L., Modelling VaR for foreign-asset portfolios in continuous time, Econ Model, 26, pp. 234-240, (2009)
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