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Simulating Sensitivities of Conditional Value at Risk

Citations

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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. Somayyeh Lotfi & Stavros A. Zenios, 2024. "Robust mean-to-CVaR optimization under ambiguity in distributions means and covariance," Review of Managerial Science, Springer, vol. 18(7), pages 2115-2140, July.
  3. L. Jeff Hong & Zhaolin Hu & Liwei Zhang, 2014. "Conditional Value-at-Risk Approximation to Value-at-Risk Constrained Programs: A Remedy via Monte Carlo," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 385-400, May.
  4. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
  5. Huang, Jinbo & Ding, Ashley & Li, Yong & Lu, Dong, 2020. "Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
  6. Wang, Bi & Chin, Kwai Sang & Su, Qin, 2022. "Prevention and adaptation to diversified risks in the seaport–dry port system under asymmetric risk behaviors: Invest earlier or wait?," Transport Policy, Elsevier, vol. 125(C), pages 11-36.
  7. Peng Wang & Rujun Jiang & Qingyuan Kong & Laura Balzano, 2026. "A Proximal Difference-of-Convex Algorithm for Sample Average Approximation of Chance Constrained Programming," INFORMS Journal on Computing, INFORMS, vol. 38(1), pages 315-339, January.
  8. Borgonovo, Emanuele & Plischke, Elmar & Rabitti, Giovanni, 2024. "The many Shapley values for explainable artificial intelligence: A sensitivity analysis perspective," European Journal of Operational Research, Elsevier, vol. 318(3), pages 911-926.
  9. Pesenti, Silvana M. & Tsanakas, Andreas & Millossovich, Pietro, 2018. "Euler allocations in the presence of non-linear reinsurance: Comment on Major (2018)," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 29-31.
  10. Huang, Xiaoxia & Ying, Haiyao, 2013. "Risk index based models for portfolio adjusting problem with returns subject to experts' evaluations," Economic Modelling, Elsevier, vol. 30(C), pages 61-66.
  11. Silvana M. Pesenti & Pietro Millossovich & Andreas Tsanakas, 2023. "Differential Quantile-Based Sensitivity in Discontinuous Models," Papers 2310.06151, arXiv.org, revised Oct 2024.
  12. Anand Deo & Karthyek Murthy, 2020. "Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives," Papers 2008.09818, arXiv.org.
  13. Peng, Yijie & Fu, Michael C. & Hu, Jiaqiao & L’Ecuyer, Pierre & Tuffin, Bruno, 2025. "Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs," European Journal of Operational Research, Elsevier, vol. 321(2), pages 493-502.
  14. Yijie Peng & Chun-Hung Chen & Michael C. Fu & Jian-Qiang Hu & Ilya O. Ryzhov, 2021. "Efficient Sampling Allocation Procedures for Optimal Quantile Selection," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 230-245, January.
  15. da Costa, B. Freitas Paulo & Pesenti, Silvana M. & Targino, Rodrigo S., 2023. "Risk budgeting portfolios from simulations," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1040-1056.
  16. Bernd Heidergott & Warren Volk-Makarewicz, 2016. "A Measure-Valued Differentiation Approach to Sensitivities of Quantiles," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 293-317, February.
  17. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2025. "Differential quantile-based sensitivity in discontinuous models," European Journal of Operational Research, Elsevier, vol. 322(2), pages 554-572.
  18. He, Zhijian, 2022. "Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo," European Journal of Operational Research, Elsevier, vol. 298(1), pages 229-242.
  19. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2013. "CVaR sensitivity with respect to tail thickness," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 977-988.
  20. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
  21. Michael C. Fu & L. Jeff Hong & Jian-Qiang Hu, 2009. "Conditional Monte Carlo Estimation of Quantile Sensitivities," Management Science, INFORMS, vol. 55(12), pages 2019-2027, December.
  22. Ronny Baierl, 2018. "Understanding Entrepreneurial Team Decisions: Measuring Team Members’ Influences With The Metricized Limit Conjoint Analysis," SAGE Open, , vol. 8(2), pages 21582440187, May.
  23. Guangxin Jiang & Michael C. Fu, 2015. "Technical Note—On Estimating Quantile Sensitivities via Infinitesimal Perturbation Analysis," Operations Research, INFORMS, vol. 63(2), pages 435-441, April.
  24. repec:cte:wsrepe:38369 is not listed on IDEAS
  25. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty," Applied Energy, Elsevier, vol. 356(C).
  26. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
  27. Peter W. Glynn & Yijie Peng & Michael C. Fu & Jian-Qiang Hu, 2021. "Computing Sensitivities for Distortion Risk Measures," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1520-1532, October.
  28. Xi Chen & Kyoung-Kuk Kim, 2016. "Efficient VaR and CVaR Measurement via Stochastic Kriging," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 629-644, November.
  29. L. Jeff Hong & Yi Yang & Liwei Zhang, 2011. "Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach," Operations Research, INFORMS, vol. 59(3), pages 617-630, June.
  30. Yijie Peng & Michael C. Fu & Bernd Heidergott & Henry Lam, 2020. "Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling," Operations Research, INFORMS, vol. 68(6), pages 1896-1912, November.
  31. Qidong Lai & Guangwu Liu & Bingfeng Zhang & Kun Zhang, 2025. "Simulating Confidence Intervals for Conditional Value-at-Risk via Least-Squares Metamodels," INFORMS Journal on Computing, INFORMS, vol. 37(4), pages 1087-1105, July.
  32. repec:aen:journl:ej37-1-bunn is not listed on IDEAS
  33. Millossovich, Pietro & Tsanakas, Andreas & Wang, Ruodu, 2024. "A theory of multivariate stress testing," European Journal of Operational Research, Elsevier, vol. 318(3), pages 851-866.
  34. Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
  35. Yan Qu & Angelos Dassios & Anxin Liu & Hongbiao Zhao, 2025. "Exact Simulation of Quadratic Intensity Models," INFORMS Journal on Computing, INFORMS, vol. 37(5), pages 1182-1201, September.
  36. Kellner, Ralf & Rösch, Daniel, 2016. "Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk," Journal of Economic Dynamics and Control, Elsevier, vol. 68(C), pages 45-63.
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