Kernel estimation of quantile sensitivities

Naval Research Logistics

Volumn 56, Issue 6, 2009, Pages 511-525

  Liu, Guangwu ^a   Hong, Liu Jeff ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Kernel method; Quantile; Sensitivity analysis; Simulation

Indexed keywords

FINANCIAL INDUSTRY; INPUT PARAMETER; KERNEL ESTIMATION; KERNEL ESTIMATORS; KERNEL METHOD; NUMERICAL EXPERIMENTS; QUANTILE; SIMULATION; STEADY-STATE SIMULATIONS; STOCHASTIC SIMULATIONS;

ESTIMATION; NORMAL DISTRIBUTION; RISK ANALYSIS; RISK MANAGEMENT; RISK PERCEPTION; STOCHASTIC MODELS;

SENSITIVITY ANALYSIS;

EID: 68949086408     PISSN: 0894069X     EISSN: 15206750     Source Type: Journal    
DOI: 10.1002/nav.20358     Document Type: Article

References (37)

1. P. Jorion, Value at risk, 2nd ed., Wiley, New York, NY, 2001.
2. P. Austin and M. Schull, Quantile regression: A statistical tool for out-of-patient research, Acad Emergency Med 10 (2003), 789-797.
3. M.C. Fu, "Gradient estimation," Handbooks in operations research and management science: simulation, S.G. Henderson and B.L. Nelson (Editors), Elsevier, Amsterdam, 2006.
4. L. Ecuyer, An overview of derivative estimation, Proceedings of the 1991 winter simulation conference, 1991, pp. 207-217.
5. C. Gourieroux, J.P. Laurent, and O. Scaillet, Sensitivity analysis of values at risk, J Empirical Finance 7 (2000), 225-245.
6. D. Tasche, Capital allocation for credit portfolios with kernel estimators, Working paper, Deutsche Bundesbank, Frankfurt, Germany, 2006.
7. E. Epperlein and A. Smillie, Cracking VAR with kernels, Risk 19 (2006), 70-74.
8. P. Glasserman, Measuring marginal risk contributions in credit portfolios, J Comput Finance 9 (2005), 1-41.
9. L.J. Hong, Estimating quantile sensitivities, Oper Res 57 (2009), 118-130.
10. L.J. Hong and G. Liu, Simulating sensitivities of conditional value at risk, Management Sci 55 (2009), 281-293.
11. L.J. Hong and G. Liu, Estimating probability sensitivities through terminating and steady-state simulations, Working paper, Department of Industrial Engineering and Logistics Management, The Hong Kong University of Science and Technology, 2008.
12. M. Broadie and P. Glasserman, Estimating security price derivatives using simulation, Management Sci 42 (1996), 269-285.
13. P. Glasserman, Gradient estimation via perturbation analysis, Kluwer Academic Publishers, Boston, 1991.
14. D. Bosq, Nonparametric statistics for stochastic processes, Springer, New York, 1998.
15. Q. Li and J.S. Racine, Nonparametric econometrics: theory and practice, Princeton Unviersity Press, New Jersey, 2007.
16. A. Pagan and A. Ullah, Nonparametric econometrics, Cambridge, UK, 1999.
17. R.J. Serfling, Approximation theorems of mathematical statistics, Wiley, New York, 1980.
18. E.F. Schuster, Joint asymptotic distribution of the estimated regression function at a finite number of distinct points, Ann Math Stat 43 (1972), 84-88.
19. G.S. Watson, Smoothing regression analysis, Sankhya Ser A (1964), 339-372.
20. G.G. Roussas, Nonparametric regression estimation under mixing conditions, Stochastic Process Appl 36 (1990), 107-116.
21. G.G. Roussas and L.T. Tran, Asymptotic normality of the recursive kernel regression estimate under dependence conditions, Ann Stat 20 (1992), 98-120.
22. Y.K. Truong, Nonparametric curve estimation with time series errors, J Stat Planning Inference 28 (1991), 167-183.
23. A.M. Law and W.D. Kelton, Simulation modeling and analysis, 3rd ed., McGraw-Hill, Boston, 2000.
24. G. Liu and L.J. Hong, Supplementary materials to "Kernel estimation of quantile sensitivities," 2009, Available at: http://www.ielm.ust.hk/ dfaculty/hongl/.
25. R. Durrett, Probability: theory and examples, 3rd ed., Duxbur Press, Belmont, 2005.
26. P. Billingsley, Convergence of probability measures, 2nd ed., Wiley, New York, 1999.
27. P. Billingsley, Convergence of probability measures, 1st ed., Wiley, New York, 1968.
28. P.W. Glynn and D.L. Iglehart, Large-sample theory for standardized time series: an overview, Proceedings of the 1985 Winter Simulation Conference, San Francisco, California, 1985.
29. P. Heidelberger and P.A. Lewis, Quantile estimation in dependent sequences, Oper Res 32 (1984), 185-209.
30. C. Chien, D. Goldsman, and B. Melamed, Large-sample results for batch means, Management Sci 43 (1997), 1288-1295.
31. L. Schruben, Confidence interval estimation using standardized time series, Oper Res 31 (1983), 1090-1108.
32. J.E. Marsden and M.J. Hoffman, Elementary classical analysis, 2nd ed., Freeman, New York, 1993.
33. H.A. David, Order statistics, 2nd ed., Wiley, New York, 1981.
34. P. Glasserman and S. Tayur, Sensitivity analysis for base- stock levels in multiechelon production-inventory systems, Management Sci 41 (1995), 263-281.
35. S.M. Ross, Stochastic processes, 2nd ed., Wiley, New York, 1996.
36. E. Parzen, On estimation of a probability density function and mode, Ann Math Stat 33 (1962), 1065-1076.
37. P.K. Sen, On the Bahadur representation of sample quantiles for sequences of ø-mixing random variables, J Multivariate Anal 2 (1972), 77-95.