Intro -- Contents -- Dedication -- Preface -- 1. Introduction -- 1.1. Formulating the Risk Problem -- 1.2. Decision Criteria -- 1.3. Decision Making Under Risk: Fact and Fiction -- 2. Probability Theory - A Mathematical Basis for Making Decisions Under Risk and Uncertainty -- 2.1. Set Theory and Probability -- 2.2. Random Variables -- 2.3. Conditional Probability and Independence -- 2.4. Some Useful Distribution Functions -- 2.5. Expected Value, Moments, and the Moment Generating Function -- 2.6. Estimating Probability Functions -- 2.6.1. Eliciting Subjective Probability Functions -- 2.6.2. Objective Estimation of Probability Functions -- 2.6.3. Production Functions and Trends -- 2.7. Martingales and Random Walks -- 2.8. Summary -- 3. Expected Utility - The Economic Basis of Decision Making Under Risk -- 3.1. Consumption and Utility -- 3.2. Expected Utility -- 3.2.1. Intuition behind Von Neumann and Morgenstern Proof -- 3.2.2. Conceptual Structure of the Axiomatic Treatment of Numerical Utilities -- 3.2.3. Analytical Examples of Expected Utility -- 3.3. Expected Value - Variance and Expected Utility Models -- 3.4. Problems with Expected Utility -- 3.5. Summary -- 4. Risk Aversion in the Large and Small -- 4.1. Arrow-Pratt Risk Aversion Coefficient -- 4.1.1. Local Risk Aversion -- 4.1.2. Transformations of Scale for the Arrow-Pratt Absolute Risk Aversion Coefficient -- 4.2. Eliciting Risk Aversion Coefficients -- 4.2.1. Direct Elicitation of Utility Functions -- 4.2.2. Equally Likely Risky Prospect and Finding Its Certainty Equivalent (ELCE) -- 4.3. Summary -- 5. Portfolio Theory and Decision Making Under Risk -- 5.1. The Expected Value - Variance Frontier -- 5.2. A Simple Portfolio -- 5.3. A Graphical Depiction of the Expected Value-Variance Frontier -- 5.4. Mean-Variance versus Direct Utility Maximization.

5.5. Derivation of the Expected Value-Variance Frontier -- 5.5.1. Derivation without a Risk-Free Asset -- 5.5.2. Derivation with a Risk-Free Asset -- 5.6. Summary -- 6. Whole Farm-Planning Models -- 6.1. Farm Portfolio Models -- 6.1.1. Gains to Diversification Using Certainty Equivalence -- 6.1.2. Extension to a Multiperiod Portfolio -- 6.2. Minimize Total Absolute Deviation -- 6.3. Focus-Loss -- 6.4. Target MOTAD -- 6.5. Direct Utility Maximization -- 6.6. Discrete Sequential Stochastic Programming -- 6.7. Chance-Constrained Programming -- 6.8. Interpreting Shadow Values from Risk Programming Models -- 6.9. Summary -- 7. Risk Efficiency Approaches - Stochastic Dominance -- 7.1. Stochastic Dominance -- 7.1.1. The Concept of an Efficiency Criteria -- 7.1.1.1. First-Degree Stochastic Dominance -- 7.1.1.2. Second-Degree Stochastic Dominance -- 7.1.2. Increasing Risk -- 7.1.2.1. Definition Based on Unanimous Preference -- 7.1.2.2. Mean Preserving Spread -- 7.1.2.3. Risk Aversion with Respect to a Function -- 7.2. Applications of Stochastic Dominance -- 7.3. Summary -- 8. Dynamic Decision Rules and the Value of Information -- 8.1. Decision Making and Bayesian Probabilities -- 8.2. Concepts of Information -- 8.3. A Model of Information -- 8.4. Summary -- 9. Market Models of Decision Making under Risk -- 9.1. Risk Equilibrium from the Consumer's Point ofView -- 9.2. The Role of the Riskless Asset -- 9.3. Risk Equilibrium from the Firm's Perspective -- 9.3.1. Deriving the Security Market Line -- 9.3.2. Supply of Stocks from the Firm -- 9.3.3. Tests of the CAPM -- 9.3.4. Incorporating Risk using CAPM -- 9.4. Arbitrage Pricing Theorem -- 9.4.1. Single-Factor Model -- 9.4.2. Two-Factor Model -- 9.5. Empirical Applications of Capital Market Models -- 9.5.1. Capital Asset Pricing Models -- 9.5.2. Tests for CAPM Efficiency -- 9.5.3. Cross-Section Regression.

9.5.4. Arbitrage Pricing Model -- 9.6. Summary -- 10. Option Pricing Approaches to Risk -- 10.1. Introduction to Options and Futures -- 10.1.1. Futures and the Hedge -- 10.1.2. Options -- 10.1.3. Option Pricing using Black-Scholes -- 10.2. Real Option Valuation -- 10.2.1. Derivation of the Value of Waiting -- 10.2.2. Application to Citrus -- 10.3. Crop Insurance -- 10.4. Summary -- 11. State Contingent Production Model: The Stochastic Production Set -- 11.1. Depicting Risk and Input Decisions in the Production Function -- 11.1.1. Estimation of Stochastic Production Functions using Quantile Regression -- 11.1.2. Developing the State-Space Representation -- 11.2. State Production Set and Input Requirement Set -- 11.3. Distance Functions and Risk Aversion -- 11.3.1. Defining the Distance Function in State-Contingency Space -- 11.3.2. Risk Aversion and Valuing States -- 11.3.3. Duality, Benefit, and Distance Functions -- 11.3.4. Defining Risk Aversion Graphically -- 11.3.5. Constant Relative and Absolute Risk Aversion -- 11.3.5.1. Risk Premium -- 11.3.5.2. Derivation of the Effort Function -- 11.4. Summary -- 12. Risk, Uncertainty, and the Agricultural Firm - A Summary and Outlook -- Appendix A. Measure Theory and the Justification of Random Variables -- Appendix B. Derivation of the Moments of the Inverse Hyperbolic Sine Distribution -- Appendix C. Numerical Techniques for Applied Optimization and Solution of Nonlinear Systems of Equations -- Appendix D. An Axiomatic Development of Expected Utility -- Appendix E. A GAMS Program to Select Optimal Portfolios -- Appendix F. R Program to Derive Optimum Portfolio with and without a Risk-Free Asset -- Appendix G. Program to Compute the Efficient Frontier with and without a Risk-Free Asset -- Appendix H. GAMS Program for the Portfolio Problem -- Bibliography -- Index.

Key Features:Focuses on the complete application of decision making under risk and uncertainty from the theory to "nuts and bolts" of applicationIncludes sample computer code using software that is readily available (i.e., freeware)Introduces the statistical basis for decision analysis under risk.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.