LEADER 04896nam 2200661 450 001 9910480819503321 005 20170821161254.0 010 $a1-4832-7399-7 035 $a(CKB)3710000000201201 035 $a(EBL)1876959 035 $a(SSID)ssj0001441866 035 $a(PQKBManifestationID)11934847 035 $a(PQKBTitleCode)TC0001441866 035 $a(PQKBWorkID)11412844 035 $a(PQKB)11619718 035 $a(MiAaPQ)EBC1876959 035 $a(EXLCZ)993710000000201201 100 $a20150106h19751975 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStochastic optimization models in finance /$fedited by W. T. Ziemba, R. G. Vickson 210 1$aNew York, New York ;$aLondon, [England] :$cAcademic Press,$d1975. 210 4$dİ1975 215 $a1 online resource (736 p.) 225 1 $aEconomic Theory and Mathematical Economics 300 $aDescription based upon print version of record. 311 $a1-322-47110-X 311 $a0-12-780850-7 320 $aIncludes bibliographical references and index at the end of each chapters. 327 $aFront Cover; Stochastic Optimization Models in Finance; Copyright Page; Dedication; Table of Contents; PREFACE; ACKNOWLEDGMENTS; Part I: Mathematical Tools; INTRODUCTION; I. Expected Utility Theory; II. Convexity and the Kuhn-Tucker Conditions; III. Dynamic Programming; SECTION1: EXPECTED UTILITY THEORY; CHAPTER 1. A GENERAL THEORY OF SUBJECTIVE PROBABILITIESAND EXPECTED UTILITIES; 1.Introduction; 2. Definitions andnotation; 3. Axioms and summarytheorem; 4.Theorems; 5. Proof of Theorem3; 6. Proof of Theorem4; SECTION2: CONVEXITY AND THE KUHN-TUCKERCONDITIONS; CHAPTER2. PSEUDO-CONVEX FUNCTIONS 327 $aAbstract1.Introduction; 2. Properties of pseudo-convex functions andapplications; 3. Remarks on pseudo-convexfunctions; 4.Acknowledgement; CHAPTER3. CONVEXITY, PSEUDO-CONVEXITY AND QUASI-CONVEXITY OF COMPOSITE FUNCTIONS; ABSTRACT; Preliminaries; Principal result; Applications; SECTION3: DYNAMIC PROGRAMMING; Chapter4. Introduction to Dynamic Programming; I. Introduction; II. Sequential Decision Processes; III. Terminating Process; IV. The Main Theorem and an Algorithm; V. Nonterminating Processes; ACKNOWLEDGMENT; REFERENCES; CHAPTER5. COMPUTATIONAL AND REVIEW EXERCISES; Exercise Source Notes 327 $aCHAPTER6. MIND-EXPANDING EXERCISESExercise Source Notes; Part II: Qualitative Economic Results; INTRODUCTION; I. Stochastic Dominance; II. Measures of Risk Aversion; III. Separation Theorems; IV. Additional Reading Material; SECTION1: STOCHASTIC DOMINANCE; Chapter 1. The Efficiency Analysis of ChoicesInvolvingRisk; I. INTRODUCTION; II. UNRESTRICTED UTILITY-THE GENERALEFFICIENCY CRITERION; III. EFFICIENCY IN THE FACE OF RISK AVERSION; IV. THE LIMITATIONS OF THE MEAN-VARIANCEEFFICIENCY CRITERION; V. CONCLUSION; REFERENCES; Chapter 2. A Unified Approach to Stochastic Dominance 327 $aI. Introduction to Stochastic DominanceII. Examples of Stochastic Dominance Relations; III. Probabilistic Content of Stochastic Dominance; REFERENCES; SECTION2: MEASURES OF RISK AVERSION; CHAPTER3. RISK AVERSION IN THE SMALL AND IN THE LARGE; 1. SUMMARY AND INTRODUCTION; 2. THE RISK PREMIUM; 3. LOCAL RISK AVERSION; 4. CONCAVITY; 5. COMPARATIVE RISK AVERSION; 6. CONSTANT RISK AVERSION; 7. INCREASING AND DECREASING RISK AVERSION; 8. OPERATIONS WHICH PRESERVE DECREASING RISK AVERSION; 9. EXAMPLES; 10. PROPORTIONAL RISK AVERSION; 11. CONSTANT PROPORTIONAL RISK AVERSION 327 $a12. INCREASING AND DECREASING PROPORTIONAL RISK AVERSION13. RELATED WORK OF ARROW; ADDENDUM; SECTION3: SEPARATION THEOREMS; CHAPTER 4. THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCKPORTFOLIOS AND CAPITAL BUDGETS; Introduction and Preview of Some Conclusions; I - Portfolio Selection for an Individual Investor:The Separation Theorem; II -Portfolio Selection: TheOptimal Stock Mix; Ill Risk Premiums and Other Properties of Stocks Held Long or Shortin Optimal Portfolios 327 $aIV - Market Prices of Shares Implied by Shareholder Optimization in Purely Competitive MarketsUnder Idealized Uncertainty 330 $aStochastic Optimization Models in Finance 410 0$aEconomic theory and mathematical economics. 606 $aFinance 606 $aMathematical optimization 606 $aStochastic processes 608 $aElectronic books. 615 0$aFinance. 615 0$aMathematical optimization. 615 0$aStochastic processes. 676 $a332.01/51922 676 $a332.0151922 702 $aZiemba$b W. T. 702 $aVickson$b R. G. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910480819503321 996 $aStochastic optimization models in finance$91131621 997 $aUNINA