The asymptotic efficiency of simulation estimators
Operations Research
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Efficient simulation for risk measurement in portfolio of CDOS
Proceedings of the 38th conference on Winter simulation
A confidence interval for tail conditional expectation via two-level simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Simulation of Coherent Risk Measures Based on Generalized Scenarios
Management Science
Nested simulation for estimating portfolio losses within a time horizon
Winter Simulation Conference
Efficient Risk Estimation via Nested Sequential Simulation
Management Science
Stochastic kriging for conditional value-at-risk and its sensitivities
Proceedings of the Winter Simulation Conference
Risk estimation via weighted regression
Proceedings of the Winter Simulation Conference
Stochastic kriging with biased sample estimates
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Risk measurement for derivative portfolios almost invariably calls for nested simulation. In the outer step, one draws realizations of all risk factors up to the horizon, and in the inner step, one reprices each instrument in the portfolio at the horizon conditional on the drawn risk factors. Practitioners may perceive the computational burden of such nested schemes to be unacceptable and adopt a variety of second-best pricing techniques to avoid the inner simulation. In this paper, we question whether such short cuts are necessary. We show that a relatively small number of trials in the inner step can yield accurate estimates, and we analyze how a fixed computational budget may be allocated to the inner and the outer step to minimize the mean square error of the resultant estimator. Finally, we introduce a jackknife procedure for bias reduction.