Semiparametric estimation of value-at-risk

Jianqing Fan Juan Gu

Statistics Theory and Methods mathscidoc:1912.43422

Value at Risk is a fundamental tool for managing market risks. It measures the worst loss to be expected of a portfolio over a given time horizon under normal market conditions 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 of the market prices of a portfolio. 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. Their statistical efficiencies are studied both theoretically and empirically. The combination of newly proposed techniques for estimating volatility and standardized quantiles yields several new techniques for forecasting 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 studies endorse the newly proposed time-dependent semiparametric approach for estimating VaR.
No keywords uploaded!
[ Download ] [ 2019-12-21 11:43:30 uploaded by Jianqing_Fan ] [ 544 downloads ] [ 0 comments ]
  title={Semiparametric estimation of value-at-risk},
  author={Jianqing Fan, and Juan Gu},
Jianqing Fan, and Juan Gu. Semiparametric estimation of value-at-risk. 2003.
Please log in for comment!
Contact us: | Copyright Reserved