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What you should know about simulation and derivatives

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  • Michael C. Fu

Abstract

Derivatives (or gradients) are important for both sensitivity analysis and optimization, and in simulation models, these can often be estimated efficiently using various methods other than brute‐force finite differences. This article briefly summarizes the main approaches and discusses areas in which the approaches can most fruitfully be applied: queueing, inventory, and finance. In finance, the focus is on derivatives of another sort. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

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  • Michael C. Fu, 2008. "What you should know about simulation and derivatives," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(8), pages 723-736, December.
    • Handle: RePEc:wly:navres:v:55:y:2008:i:8:p:723-736
    DOI: 10.1002/nav.20313
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    References listed on IDEAS

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    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Sridhar Bashyam & Michael C. Fu, 1994. "Application of perturbation analysis to a class of periodic review (s, S) inventory systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 47-80, February.
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    8. Heidergott, Bernd, 1999. "Optimisation of a single-component maintenance system: A smoothed perturbation analysis approach," European Journal of Operational Research, Elsevier, vol. 119(1), pages 181-190, November.
    9. Rongwen Wu & Michael C. Fu, 2003. "Optimal Exercise Policies and Simulation-Based Valuation for American-Asian Options," Operations Research, INFORMS, vol. 51(1), pages 52-66, February.
    10. Michael C. Fu & Jian-Qiang Hu, 1999. "Efficient Design and Sensitivity Analysis of Control Charts Using Monte Carlo Simulation," Management Science, INFORMS, vol. 45(3), pages 395-413, March.
    11. Michael C. Fu, 1994. "Sample Path Derivatives for (s, S) Inventory Systems," Operations Research, INFORMS, vol. 42(2), pages 351-364, April.
    12. Chen, Nan & Glasserman, Paul, 2007. "Malliavin Greeks without Malliavin calculus," Stochastic Processes and their Applications, Elsevier, vol. 117(11), pages 1689-1723, November.
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    Cited by:

    1. L. Jeff Hong & Sandeep Juneja & Jun Luo, 2014. "Estimating Sensitivities of Portfolio Credit Risk Using Monte Carlo," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 848-865, November.
    2. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Eric Bergerson & Megan Kurka & Ludek Kopacek, 2020. "Learning Demand Curves in B2B Pricing: A New Framework and Case Study," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1287-1306, May.
    3. Lingyan Cao & Zheng-Feng Guo, 2012. "A Comparison Of Gradient Estimation Techniques For European Call Options," Accounting & Taxation, The Institute for Business and Finance Research, vol. 4(1), pages 75-81.
    4. Honggang Wang, 2017. "Subspace dynamic‐simplex linear interpolation search for mixed‐integer black‐box optimization problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 305-322, June.
    5. Zhenyu Cui & Michael C. Fu & Jian-Qiang Hu & Yanchu Liu & Yijie Peng & Lingjiong Zhu, 2020. "On the Variance of Single-Run Unbiased Stochastic Derivative Estimators," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 390-407, April.
    6. Zhaolin Hu & Dali Zhang, 2018. "Utility‐based shortfall risk: Efficient computations via Monte Carlo," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(5), pages 378-392, August.
    7. Michael C. Fu & Huashuai Qu, 2014. "Regression Models Augmented with Direct Stochastic Gradient Estimators," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 484-499, August.

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