Monte carlo computation of conditional expectation quantiles
Monte carlo computation of conditional expectation quantiles
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th 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
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
Nested Simulation in Portfolio Risk Measurement
Management Science
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We consider the problem of estimating the probability that a stochastic process observed at discrete time intervals exceeds a specified threshold. We further assume that the value of this process at any time, along any realization, is a conditional expectation which is not known analytically but can be estimated via simulation. This leads to a nested simulation procedure. One application of this arises in risk management where our interest may be in the probability that a portfolio exceeds a threshold of losses at specified times. Here, if the portfolio consists of sophisticated derivatives, then as a function of the underlying security prices, the portfolio value at any time is a conditional expectation that may be evaluated via simulation. In our analysis, we note that conditional on the outer loop of simulation, our estimation problem is related to the large deviations based ordinal optimization framework, so that similar analysis may be used to efficiently allocate computational budget in portfolio evaluations at different times. We also propose a resource allocation methodology based on statistical hypothesis testing.