Estimating security price derivatives using simulation
Management Science
Variance Reduction Techniques for Estimating Value-at-Risk
Management Science
Simulating Sensitivities of Conditional Value at Risk
Management Science
Stochastic Kriging for Simulation Metamodeling
Operations Research
Nested Simulation in Portfolio Risk Measurement
Management Science
Efficient Risk Estimation via Nested Sequential Simulation
Management Science
Mathematical and Computer Modelling: An International Journal
Stochastic kriging with biased sample estimates
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Measuring risks in asset portfolios has been one of the central topics in the financial industry. Since the introduction of coherent risk measures, studies on risk measurement have flourished and measures beyond value-at-risk, such as expected shortfall, have been adopted by academics and practitioners. However, the complexity of financial products makes it very difficult and time consuming to perform the numerical tasks necessary to compute these risk measures. In this paper, we introduce a stochastic kriging metamodel-based method for efficient estimation of risks and their sensitivities. In particular, this method uses gradient estimators of assets in a portfolio and gives the best linear unbiased predictor of the risk sensitivities with minimum mean squared error. Numerical comparisons of the proposed method with two other stochastic kriging based approaches demonstrate the promising role that the proposed method can play in the estimation of financial risk.