LEADER 05749nam 2200757Ia 450 001 9910826068703321 005 20240313121710.0 010 $a1-283-90005-X 010 $a981-4407-51-8 035 $a(CKB)3280000000002159 035 $a(EBL)1109708 035 $a(OCoLC)826853976 035 $a(SSID)ssj0000760150 035 $a(PQKBManifestationID)12341618 035 $a(PQKBTitleCode)TC0000760150 035 $a(PQKBWorkID)10810846 035 $a(PQKB)11230280 035 $a(MiAaPQ)EBC1109708 035 $a(WSP)00002839 035 $a(Au-PeEL)EBL1109708 035 $a(CaPaEBR)ebr10640602 035 $a(EXLCZ)993280000000002159 100 $a20120713d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStochastic programming $eapplications in finance, energy, planning and logistics /$f[edited by] Horand Gassmann, William Ziemba 205 $a1st ed. 210 $aSingapore ;$aHackensack, NJ $cWorld Scientific$dc2013 215 $a1 online resource (549 p.) 225 0 $aWorld Scientific series in finance ;$v4 300 $aDescription based upon print version of record. 311 $a981-4407-50-X 320 $aIncludes bibliographical references and index. 327 $aAcknowledgements; List of Contributors; Preface; Books and Collections of Papers on Stochastic Programming; Contents; 1. Introduction and Summary; Part I. Papers in Finance; 2. Longevity Risk Management for Individual Investors Woo Chang Kim, John M. Mulvey, Koray D. Simsek and Min Jeong Kim; 1 Introduction; 2 Model; 3 Numerical results; 3.1 First example: Retirement planning without longevity risk consideration; 3.2 Second example: Impact of longevity risk to retirement planning; 3.3 Third example: Longevity risks in pension benefits; 4 Conclusions; References 327 $a3. Optimal Stochastic Programming-Based Personal Financial Planning with Intermediate and Long-Term Goals Vittorio Moriggia, Giorgio Consigli and Gaetano Iaquinta1 Introduction; 2 The asset-liability management model; 2.1 Individual wealth, consumption and investment targets; 2.2 Random coefficients and scenarios; 2.3 The optimization problem; 3 Numerical implementation and case study; 3.1 Decision tool modular structure; 3.1.1 Individual policy statement; 3.1.2 Scenario manager; 3.1.3 Output; 3.2 Case study; 3.2.1 Optimal solutions; 4 Conclusion; References 327 $a4. Intertemporal Surplus Management with Jump Risks Mareen Benk1 Introduction; 2 An intertemporal surplus management model with jump risks - a three-fund theorem; 3 Risk preference, and funding ratio; 4 Conclusions; Appendix I: Derivation of the asset specific risk factor of the first jump component; Appendix II: Derivation of equation (16); Appendix III: Derivation of equation (17); References; 5. Jump-Diffusion Risk-Sensitive Benchmarked Asset Management Mark Davis and Sebastien Lleo; 1 Introduction; 2 Analytical setting; 2.1 Factor dynamics; 2.2 Asset market dynamics 327 $a2.3 Benchmark modelling2.4 Portfolio dynamics; 2.5 Investment constraints; 2.6 Problem formulation; 3 Dynamic programming and the value function; 3.1 The risk-sensitive control problems under Ph; 3.2 Properties of the value function; 3.3 Main result; 4 Existence of a classical (C1,2) solution under affine drift assumptions; 5 Existence of a classical (C1,2) solution under standard control assumptions; 6 Verification; 6.1 The unique maximizer of the supremum (60) is the optimal control, i.e. h*(t,Xt) = h (t,Xt,D (t,Xt)); 6.2 Verification; 7 Conclusion; References 327 $a6. Dynamic Portfolio Optimization under Regime-Based Firm Strength Chanaka Edirisinghe and Xin Zhang1 Introduction; 2 DEA-based relative firm strength; 2.1 Financial DEA model; 2.2 Parameters of RFS; 2.3 Correlation analysis; 3 Modeling market regimes; 3.1 Regime analysis (1971-2010); 3.2 Regime-based firm-RFS; 4 Portfolio optimization under regime-based RFS; 4.1 RFS-based stock selections; 4.2 Decisions under regime-scenarios; 4.3 Transactions cost model; 4.4 Budget constraints; 4.5 Risk-return framework; 4.6 Two-period optimization model; 5 Model application 327 $a5.1 RFS estimation and firm selections 330 $aThis book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with ben 410 0$aWorld Scientific Series in Finance 606 $aMathematical optimization 606 $aMathematical optimization$xIndustrial applications 606 $aStochastic processes$xEconometric models 606 $aStochastic programming 606 $aDecision making 606 $aUncertainty 615 0$aMathematical optimization. 615 0$aMathematical optimization$xIndustrial applications. 615 0$aStochastic processes$xEconometric models. 615 0$aStochastic programming. 615 0$aDecision making. 615 0$aUncertainty. 676 $a519.7 701 $aGassmann$b Horand$01622306 701 $aZiemba$b W. T$0122735 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826068703321 996 $aStochastic programming$93956110 997 $aUNINA