Data-analytic approaches to the estimation of Value-at-Risk
Refereed conference paper presented and published in conference proceedings

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AbstractValue 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 prics 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 studies endorse the newly proposed time-dependent semiparametric approach for estimating VaR.
All Author(s) ListFan JQ, Gu J
Name of ConferenceIEEE International Conference on Computational Intelligence for Financial Engineering
Start Date of Conference20/03/2003
End Date of Conference23/03/2003
Place of ConferenceHONG KONG
Country/Region of ConferenceChina
Pages271 - 277
LanguagesEnglish-United Kingdom
Keywordsaggregate returns; choice of decay factor; quantile; semiparametric; Value-at-Risk; volatility
Web of Science Subject CategoriesBusiness & Economics; Business, Finance; Computer Science; Computer Science, Artificial Intelligence; Economics; Mathematics; Statistics & Probability

Last updated on 2021-27-09 at 06:33