04964nam 2200649 450 991078679600332120230228212143.01-4832-7399-7(CKB)3710000000201201(EBL)1876959(SSID)ssj0001441866(PQKBManifestationID)11934847(PQKBTitleCode)TC0001441866(PQKBWorkID)11412844(PQKB)11619718(MiAaPQ)EBC1876959(EXLCZ)99371000000020120120150106h19751975 uy 0engur|n|---|||||txtrdacontentcrdamediacrrdacarrierStochastic optimization models in finance /editors, W. T. Ziemba, R. G. VicksonNew York :Academic Press,1975.©19751 online resource (xvi, 719 pages) illustrationsEconomic Theory and Mathematical EconomicsDescription based upon print version of record.1-322-47110-X 0-12-780850-7 Includes bibliographical references and index at the end of each chapters.Front 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 FUNCTIONSAbstract1.Introduction; 2. Properties of pseudo-convex functions and applications; 3. Remarks on pseudo-convex functions; 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 NotesCHAPTER6. MIND-EXPANDING EXERCISES Exercise 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 Choices Involving Risk; 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 DominanceI. Introduction to Stochastic Dominance II. 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 AVERSION12. 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: The Optimal Stock Mix; Ill Risk Premiums and Other Properties of Stocks Held Long or Short in Optimal Portfolios; IV - Market Prices of Shares Implied by Shareholder Optimization in Purely Competitive Markets Under Idealized UncertaintyStochastic Optimization Models in FinanceEconomic theory and mathematical economics.FinanceMathematical modelsMathematical optimizationStochastic processesFinanceMathematical models.Mathematical optimization.Stochastic processes.332.01/51922332.0151922Ziemba W. T.122735Ziemba W. T.Vickson R. G.MiAaPQMiAaPQMiAaPQBOOK9910786796003321Stochastic optimization models in finance3762393UNINA