Stochastic kriging for conditional value-at-risk and its sensitivities

Proceedings - Winter Simulation Conference

Volumn , Issue , 2012, Pages

  Chen, Xi ^a   Nelson, Barry L ^a   Kim, Kyoung Kuk ^b  

^a Northwestern University   (United States)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BEST LINEAR UNBIASED PREDICTOR; COHERENT RISK MEASURES; CONDITIONAL VALUE-AT-RISK; EFFICIENT ESTIMATION; EXPECTED SHORTFALL; FINANCIAL INDUSTRY; FINANCIAL PRODUCTS; FINANCIAL RISKS; GRADIENT ESTIMATOR; KRIGING; MINIMUM MEAN SQUARED ERROR; NUMERICAL COMPARISON; RISK MEASUREMENT; RISK MEASURES; RISK SENSITIVITY; VALUE AT RISK;

ESTIMATION; FINANCE; INTERPOLATION; NUMERICAL METHODS; STOCHASTIC SYSTEMS; VALUE ENGINEERING;

RISK PERCEPTION;

EID: 84874694546     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2012.6465096     Document Type: Conference Paper

References (21)

1. Acerbi, C., and D. Tasche. 2002. "On the coherence of expected shortfall". Journal of Banking & Finance 26:1487-1503.
2. Ankenman, B., B. L. Nelson, and J. Staum. 2010. "Stochastic kriging for simulation metamodeling". Operations Research 58:371-382.
3. Artzner, P., F. Delbaen, J. M. Eber, and D. Heath. 1999. "Coherent measures of risk". Mathematical Finance 9:203-228.
4. Asmussen, S., and P. W. Glynn. 2007. Stochastic Simulation. New York: Springer.
5. Broadie, M., Y. Du, and C. C. Moallemi. 2011a. "Efficient risk estimation via nested sequential simulation". Management Science 57:1172-1194.
6. Broadie,M., Y.Du, and C. C.Moallemi. 2011b. "Risk estimation via regression". Technical report, Columbia University, New York, NY.
7. Broadie,M., and P.Glasserman. 1996. "Estimating security price derivatives using simulation".Management Science 42:269-285. (Pubitemid 126545101)
8. Chen, X., B. Ankenman, and B. L. Nelson. 2011. "Enhancing stochastic kriging metamodels with gradient estimators". Technical report, Northwestern University, Chicago, IL.
9. Fu, M. C., and J. Q. Hu. 1995. "Sensitivity analysis for Monte Carlo simulation of option pricing". Probability in the Engineering and Information Sciences 9:417-446. (Pubitemid 126018243)
10. Glasserman, P., P. Heidelberger, and P. Shahabuddin. 2000. "Variance reduction techniques for estimating value-at-risk". Management Science 46:1349-1364. (Pubitemid 32021940)
11. Gordy, M. B., and S. Juneja. 2010. "Nested simulation in portfolio risk measurement". Management Science 56:1833-1848.
12. Gourieroux, C., J. P. Laurent, and O. Scaillet. 2000. "Sensitivity analysis of Value at Risk". Journal of Empirical Finance 7:225-245.
13. Hong, L. J., and G. Liu. 2009. "Simulation sensitivities of conditional value at risk". Management Science 55:281-293.
14. Lan, H., B. L. Nelson, and J. Staum. 2010. "A confidence interval procedure for expected shortfall risk measurement via two-level simulation". Operations Research 58:1481-1490.
15. Liu, M., and J. Staum. 2010. "Stochastic kriging for efficient nested simulation of expected shortfall". Journal of Risk 12:3-27.
16. Morris,M.D., T. J.Mitchell, andD.Ylvisaker. 1993. "Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction". Technometrics 35:243-255.
17. Pardo-Igúzquiza, E., and M. Chica-Olmo. 2004. "Estimation of gradients from sparse data by universal kriging". Water Resources Research 40:1-17 W12418.
18. Parzen, E. 1962. Stochastic Processes. CA: Holden-Day.
19. Sakata, S., F. Ashida, and M. Zako. 2010. "Comparative study on gradient and Hessian estimation using the Kriging method and neural network approximation". Mathematical and Computer Modelling 51:309-319.
20. Scaillet, O. 2004. "Nonparametric estimation and sensitivity analysis of expected shortfall". Mathematical Finance 14:115-129. (Pubitemid 38203204)
21. Solak, E., R. Murray-Smith, W. E. Leithead, D. J. Leith, and C. E. Rasmussen. 2003. "Derivative observations in Gaussian Process models of dynamic systems". Advances in Neural Information Processing Systems 15:1033-1040.