A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk
| dc.contributor.author | Jin, Xing | |
| dc.contributor.author | Fu, Michael C. | |
| dc.date.accessioned | 2005-07-01T12:31:49Z | |
| dc.date.available | 2005-07-01T12:31:49Z | |
| dc.date.issued | 2005-07-01T12:31:49Z | |
| dc.description.abstract | Quantile estimation has become increasingly important, particularly in the financial industry, where Value-at-Risk has emerged as a standard measurement tool for controlling portfolio risk. In this paper we apply the theory of large deviations to analyze various simulation-based quantile estimators. First, we show that the coverage probability of the standard quantile estimator converges to one exponentially fast with sample size. Then we introduce a new quantile estimator that has a provably faster convergence rate. Furthermore, we show that the coverage probability for this new estimator can be guaranteed to be 100% with sufficiently large, but finite, sample size. Numerical experiments on a VaR example illustrate the potential for dramatic variance reduction. | en |
| dc.format.extent | 364407 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/2301 | |
| dc.language.iso | en_US | |
| dc.relation.isAvailableAt | Robert H. Smith School of Business | en_us |
| dc.relation.isAvailableAt | Decision & Information Technologies | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_us |
| dc.subject | simulation | en |
| dc.subject | quantile estimation | en |
| dc.subject | large deviations | en |
| dc.subject | value at risk | en |
| dc.title | A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk | en |
| dc.type | Article | en |