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Nonparametric Statistical Inference of Value At Risk For Financial Time Series

Author

Listed:
  • Song Xi Chen
  • Cheng Yong Tang

Abstract

The paper considers nonparametric estimation of Value at Risk (VaR) and associated standard error estimation for dependent financial return series. The presence of dependence affects the variance of the VaR estimates and has to be taken into consideration in order to obtain adequate assessment on their variation. As estimation procedure of the standard errors is proposed based on a assessment on their variation. As estimation procedure of the standard errors is proposed based on a kernel estimation of the spectral density of a derived series. The performance of the VaR estimators and the proposed standard error estimation procedure are evaluated by theoretical investigation, simulation of commonly used models for financial returns and empirical studies on real financial return series.

Suggested Citation

  • Song Xi Chen & Cheng Yong Tang, 2003. "Nonparametric Statistical Inference of Value At Risk For Financial Time Series," Research Paper Series 88, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:88
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    Cited by:

    1. Jianqing Fan, 2004. "A selective overview of nonparametric methods in financial econometrics," Papers math/0411034, arXiv.org.

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