Data-analytic approaches to the estimation of value-at-risk

Jianqing Fan Juan Gu

Statistics Theory and Methods mathscidoc:1912.43407

271-277, 2003.3
Value-at-risk measures the worst loss to be expected of a portfolio over a given time horizon at a given confidence level. Calculation of VaR frequently involves estimating the volatility of return processes and quantiles of standardized returns. In this paper, several semiparametric techniques are introduced to estimate the volatilities. In addition, both parametric and nonparametric techniques are proposed to estimate the quantiles of standardized return processes. The newly proposed techniques also have the flexibility to adapt automatically to the changes in the dynamics of market prices over time. The combination of newly proposed techniques for estimating volatility and standardized quantiles yields several new techniques for evaluating multiple period VaR. The performance of the newly proposed VaR estimators is evaluated and compared with some of existing methods. Our simulation results and empirical
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@inproceedings{jianqing2003data-analytic,
  title={Data-analytic approaches to the estimation of value-at-risk},
  author={Jianqing Fan, and Juan Gu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114236828741967},
  pages={271-277},
  year={2003},
}
Jianqing Fan, and Juan Gu. Data-analytic approaches to the estimation of value-at-risk. 2003. pp.271-277. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114236828741967.
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