Robert H. Smith School of Business >
Decision, Operations & Information Technologies >
Decision, Operations & Information Technologies Research Works >
Please use this identifier to cite or link to this item:
|Title: ||A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk|
|Authors: ||Jin, Xing|
Fu, Michael C.
value at risk
|Issue Date: ||1-Jul-2005|
|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.|
|Appears in Collections:||Decision, Operations & Information Technologies Research Works|
All items in DRUM are protected by copyright, with all rights reserved.