Data-analytic approaches to the estimation of Value-at-Risk

Jianqing Fan, Juan Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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 studies endorse the newly proposed time-dependent semiparametric approach for estimating VaR.

Original languageEnglish (US)
Title of host publication2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages271-277
Number of pages7
ISBN (Electronic)0780376544
DOIs
StatePublished - Jan 1 2003
Externally publishedYes
Event2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003 - Hong Kong, China
Duration: Mar 20 2003Mar 23 2003

Publication series

NameIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Volume2003-January

Other

Other2003 IEEE International Conference on Computational Intelligence for Financial Engineering, CIFEr 2003
CountryChina
CityHong Kong
Period3/20/033/23/03

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Finance

Keywords

  • Aggregates
  • Data security
  • Function approximation
  • Gaussian distribution
  • Loss measurement
  • Parametric statistics
  • Portfolios
  • Reactive power
  • Risk management
  • Time measurement

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