Quantiles of a random performance serve as important alternatives to the usual expected value. They are used in the
financial industry as measures of risk and in the service industry as measures of service quality. To manage the quantile of
a performance, we need to know how changes in the input parameters affect the output quantiles, which are called quantile
sensitivities. In this paper, we show that the quantile sensitivities can be written in the form of conditional expectations.
Based on the conditional-expectation form, we first propose an infinitesimal-perturbation-analysis (IPA) estimator. The IPA
estimator is asymptotically unbiased, but it is not consistent. We then obtain a consistent estimator by dividing data into
batches and averaging the IPA estimates of all batches. The estimator satisfies a central limit theorem for the i.i.d. data,
and the rate of convergence is strictly slower than n-1/3. The numerical results show that the estimator works well for
practical

Find all documents with these keywords:
marginal　contributions　incremental　sensitivities　quantile　
Find all documents in these Categories:
VaR Methods——Marginal VaR