Introduction to Algorithms
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Variance Reduction Techniques for Estimating Value-at-Risk
Management Science
Simulation of coherent risk measures
WSC '04 Proceedings of the 36th conference on Winter simulation
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
Simulation of Coherent Risk Measures Based on Generalized Scenarios
Management Science
Nested Simulation in Portfolio Risk Measurement
Management Science
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Estimating the mean of a non-linear function of conditional expectation
Winter Simulation Conference
An efficient simulation procedure for point estimation of expected shortfall
Proceedings of the Winter Simulation Conference
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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We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk estimator in each scenario. Theoretical results are given to show that the risk estimator has a faster convergence order compared to the conventional uniform inner sampling approach. Numerical results consistent with the theory are presented. This paper was accepted by Gérard Cachon, stochastic models and simulation.