Jackknife estimators for reducing bias in asset allocation

Proceedings - Winter Simulation Conference

Volumn , Issue , 2006, Pages 783-791

  Partani, Amit ^a   Morton, David P ^a   Popova, Ivilina ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b SEATTLE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FINANCE; INVESTMENTS; OPTIMAL SYSTEMS; STOCHASTIC PROGRAMMING;

ASSET ALLOCATION; CANDIDATE SOLUTION; OPTIMAL VALUES; POWER UTILITIES;

ESTIMATION;

EID: 46149111479     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2006.323159     Document Type: Conference Paper

References (22)

1. Andersen, L., and M. Broadie. 2004. A primal-dual simulation algorithm for pricing multi-dimensional American options. Management Science 50:1222-1234.
2. Attouch, H., and R.-B. Wets. 1990. Epigraphical processes: Laws of large numbers for random lsc functions. In Séminaire d'Analyse Convexe, Volume 20, 13.1-13.29. Department Des Sciences Mathématiques, Universite Montpellier II.
3. Bayraksan, G., and D. Morton. 2006. Assessing solution quality in stochastic programs. Mathematical Programming. To appear.
4. Bayraksan, G., D. Morton, and A. Partani. 2006. Simulationbased optimality tests for stochastic programs. In Planning Under Uncertainty: Stochastic Programming, ed. G. Infanger and G. Dantzig. Kluwer Series on Advances in Mathematical Programming. Forthcoming.
5. Dupačová, J., and R. Wets. 1988. Asymptotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems. The Annals of Statistics 16:1517-1549.
6. Fu, M., S. Laprise, D. Madan, Y. Su, and R. Wu. 2001. Pricing American options: A comparison of Monte Carlo simulation approaches. Journal of Computational Finance 4:39-88.
7. Glasserman, P. 2003. Monte Carlo methods in financial engineering. Springer, New York.
8. Gray, H., and W. Schucany. 1972. The generalized jackknife statistic. Marcel Dekker, Inc., New York.
9. King, A., and D. Jensen. 1992. Linear-quadratic efficient frontiers for portfolio optimization. Applied Stochastic Models and Data Analysis 8:195-207.
10. King, A., and R. Rockafellar. 1993. Asymptotic theory for solutions in statistical estimation and stochastic programming. Mathematics of Operations Research 18:148-162.
11. L'Ecuyer, P. 1999. Good parameter sets for combined multiple recursive random number generators. Operations Research 47:159-164.
12. Mak, W., D. Morton, and R. Wood. 1999. Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations Research Letters 24:47-56.
13. Morton, D., E. Popova, and I. Popova. 2006. Efficient fund of hedge funds construction under downside risk measures. Journal of Banking and Finance 30:503-518.
14. Morton, D., E. Popova, I. Popova, and M. Zhong. 2003. Optimizing benchmark-based utility functions. Bulletin of the Czech Econometric Society 10:1-18.
15. Norkin, V., G. Pflug, and A. Ruszczyñski. 1998. A branch and bound method for stochastic global optimization. Mathematical Programming 83:425-450.
16. Partani, A. Simulation-based procedures for efficient assessment of solution quality in stochastic programming. Ph. D. thesis, The University of Texas at Austin. In progress.
17. Quenouille, M. 1949a. Approximate tests of correlation in time-series. Journal of the Royal Statistical Society, Series B 11:68-84.
18. Quenouille, M. 1949b. Problems in plane sampling. Annals of Mathematical Statistics 20:355-375.
19. Schruben, L. 1980. Coverage function of interval estimators of simulation response. Management Science 26:18-27.
20. Shapiro, A. 2003. Monte Carlo sampling methods. In Stochastic Programming, Handbooks in Operations Research and Management Science, ed. A. Ruszczyñski and A. Shapiro. Elsevier, Amsterdam.
21. Wächter, A., and L. Biegler. 2006. On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming 106:25-57.
22. Ziemba, W., and J. Mulvey. (Eds.) 1998. Worldwide asset and liability modeling. Cambridge University Press, Cambridge, United Kingdom.