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Title: Risk objectives in two-stage stochastic programming models (English)
Author: Dupačová, Jitka
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 44
Issue: 2
Year: 2008
Pages: 227-242
Summary lang: English
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Category: math
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Summary: In applications of stochastic programming, optimization of the expected outcome need not be an acceptable goal. This has been the reason for recent proposals aiming at construction and optimization of more complicated nonlinear risk objectives. We will survey various approaches to risk quantification and optimization mainly in the framework of static and two-stage stochastic programs and comment on their properties. It turns out that polyhedral risk functionals introduced in Eichorn and Römisch [Eich-Ro] have many convenient features. We shall complement the existing results by an application of contamination technique to stress testing or robustness analysis of stochastic programs with polyhedral risk objectives with respect to the underlying probability distribution. The ideas will be illuminated by numerical results for a bond portfolio management problem. (English)
Keyword: two-stage stochastic programs
Keyword: polyhedral risk objectives
Keyword: robustness
Keyword: contamination
Keyword: bond portfolio management problem
MSC: 90C15
MSC: 90C39
MSC: 91B28
MSC: 91B30
MSC: 93E20
idZBL: Zbl 1154.91500
idMR: MR2428221
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Date available: 2009-09-24T20:33:41Z
Last updated: 2012-06-06
Stable URL: http://hdl.handle.net/10338.dmlcz/135845
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Reference: [1] Acerbi C.: Spectral measures of risk: A coherent representation of subjective risk aversion.J. Bank. Finance 26 (2002), 1505–1518
Reference: [2] Ahmed S.: Convexity and decomposition of mean-risk stochastic programs.Math. Programming A 106 (2006), 447–452 Zbl 1134.90025, MR 2216788
Reference: [3] Artzner P., Delbaen F., Eber, J., Heath D.: Coherent measures of risk.Math. Finance 9 (1999), 203–228. See also , pp. 145–175 Zbl 0980.91042, MR 1850791
Reference: [4] Bertocchi M., Dupačová, J., Moriggia V.: Sensitivity analysis of a bond portfolio model for the Italian market.Control Cybernet. 29 (2000), 595–615 Zbl 1017.91036
Reference: [5] Bertocchi M., Moriggia, V., Dupačová J.: Horizon and stages in applications of stochastic programming in finance.Ann. Oper. Res. 142 (2006), 63–78 Zbl 1101.90043, MR 2222910
Reference: [6] Dempster M. A. H., ed.: Risk Management: Value at Risk and Beyond.Cambridge Univ. Press, Cambridge 2002 Zbl 1213.91087, MR 1892188
Reference: [7] Dupačová J.: Stability in stochastic programming with recourse – contaminated distributions.Math. Programing Stud. 27 (1986), 133–144 Zbl 0594.90068, MR 0836754
Reference: [8] Dupačová J.: Stability and sensitivity analysis in stochastic programming.Ann. Oper. Res. 27 (1990), 115–142 MR 1088990
Reference: [9] Dupačová J.: Postoptimality for multistage stochastic linear programs.Ann. Oper. Res. 56 (1995), 65–78 Zbl 0838.90089, MR 1339785
Reference: [10] Dupačová J.: Scenario based stochastic programs: Resistance with respect to sample.Ann. Oper. Res. 64 (1996), 21–38 Zbl 0854.90107, MR 1405628
Reference: [11] Dupačová J.: Reflections on robust optimization.In: Stochastic Programming Methods and Technical Applications (K. Marti and P. Kall, eds.), LNEMS 437, Springer, Berlin 1998, pp. 111–127 Zbl 0909.90218, MR 1650772
Reference: [12] Dupačová J.: Stress testing via contamination.In: Coping with Uncertainty. Modeling and Policy Issues (K. Marti et al., eds.), LNEMS 581, Springer, Berlin 2006, pp. 29–46 Zbl 1151.90504, MR 2278935
Reference: [13] Dupačová J.: Contamination for multistage stochastic programs.In: Prague Stochastics 2006 (M. Hušková and M. Janžura, eds.), Matfyzpress, Praha 2006, pp. 91–101. See also SPEPS 2006-06
Reference: [14] Dupačová J., Bertocchi, M., Moriggia V.: Testing the structure of multistage stochastic programs.Submitted to Optimization Zbl 1168.90567
Reference: [15] Dupačová J., Hurt, J., Štěpán J.: Stochastic Modeling in Economics and Finance, Part II.Kluwer Academic Publishers, Dordrecht 2002 MR 2008457
Reference: [16] Dupačová J., Polívka J.: Stress testing for VaR and CVaR.Quantitative Finance 7 (2007), 411–421 Zbl 1180.91163, MR 2354778
Reference: [17] Eichhorn A., Römisch W.: Polyhedral risk measures in stochastic programming.SIAM J. Optim. 16 (2005), 69–95 Zbl 1114.90077, MR 2177770
Reference: [18] Eichhorn A., Römisch W.: Mean-risk optimization models for electricity portfolio management.In: Proc. 9th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2006), Stockholm 2006
Reference: [19] Eichhorn A., Römisch W.: Stability of multistage stochastic programs incorporating polyhedral risk measures.To appear in Optimization 2008 Zbl 1192.90131, MR 2400373
Reference: [20] Föllmer H., Schied A.: Stochastic Finance.An Introduction in Discrete Time. (De Gruyter Studies in Mathematics 27). Walter de Gruyter, Berlin 2002 Zbl 1126.91028, MR 1925197
Reference: [21] Kall P., Mayer J.: Stochastic Linear Programming.Models, Theory and Computation. Springer-Verlag, Berlin 2005 Zbl 1211.90003, MR 2118904
Reference: [22] Mulvey J. M., Vanderbei R. J., Zenios S. A.: Robust optimization of large scale systems.Oper. Res. 43 (1995), 264–281 Zbl 0832.90084, MR 1327415
Reference: [23] Ogryczak W., Ruszczyński A.: Dual stochastic dominance and related mean-risk models.SIAM J. Optim. 13 (2002), 60–78 Zbl 1022.91017, MR 1922754
Reference: [24] Pflug G. Ch.: Some remarks on the Value-at-Risk and the Conditional Value-at-Risk.In: Probabilistic Constrained Optimization, Methodology and Applications (S. Uryasev, ed.), Kluwer Academic Publishers, Dordrecht 2001, pp. 272–281 Zbl 0994.91031, MR 1819417
Reference: [25] Pflug G. Ch., Römisch W.: Modeling, Measuring and Managing Risk.World Scientific, Singapur 2007 Zbl 1153.91023, MR 2424523
Reference: [26] Rockafellar R. T., Uryasev S. : Conditional value-at-risk for general loss distributions.J. Bank. Finance 26 (2001), 1443–1471
Reference: [27] Rockafellar R. T., Uryasev, S., Zabarankin M.: Generalized deviations in risk analysis.Finance Stochast. 10 (2006), 51–74 Zbl 1150.90006, MR 2212567
Reference: [28] Römisch W.: Stability of stochastic programming problems.Chapter 8 in , pp. 483–554 MR 2052760
Reference: [29] Römisch W., Wets R. J-B.: Stability of $\varepsilon $-approximate solutions to convex stochastic programs.SIAM J. Optim. 18 (2007), 961–979 Zbl 1211.90151, MR 2345979
Reference: [30] Ruszczyński A., Shapiro A., eds.: Handbook on Stochastic Programming.Handbooks in Operations Research & Management Science 10, Elsevier, Amsterdam 2002
Reference: [31] Ruszczyński A., Shapiro A.: Optimization of risk measures.Chapter 4 in: Probabilistic and Randomized Methods for Design under Uncertainty (G. Calafiore and F. Dabbene, eds.), Springer, London 2006, pp. 121–157 Zbl 1181.90281
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