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Converting Risk Preferences into Money Equivalents with Quadratic Programming

Explore how risk preferences can be converted into monetary values using quadratic programming, allowing managers to make informed decisions. Discuss concepts such as utility functions, certainty equivalents, and risk premiums.

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Converting Risk Preferences into Money Equivalents with Quadratic Programming

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  1. Converting Risk Preferences into Money Equivalents with Quadratic Programming AEC 851 – Agribusiness Operations Management Spring, 2006

  2. Expected Utility Model • A numerical “utility” value can be linked to any risky prospect if a manager’s preferences meet these conditions: • Can be ordered • Are transitive • Are continuous • Are independent of irrelevant alternatives

  3. Key results from EUM • A manager is risk averse if he or she prefers the expected outcome of a risky prospect to the risky prospect itself • So utility function is concave • Certainty equivalent (xCE) is value that would leave manager indifferent between that and expected outcome • E[U(x)] = U(xCE)

  4. Utility function showing risk aversity, certainty equivalent and risk premium Source: Boisvert & McCarl (1990)

  5. Risk premium () is amount a risk-averse manager would be willing to pay to avoid a risky prospect: • U(E[x- ]) = E[U(x)]

  6. EU functions • Risk aversion is shown by the degree of curvature of the utility function • Math functions exist that characterize • Constant absolute risk aversion (constant rate of curvature of utility function) (CARA) • Constant relative risk aversion (constant rate of risk aversion relative to total wealth) (CRRA) • However, these functions have limitations: • 1) Complicated forms for certainty equivalent • 2) Not clear how many people’s preferences are accurately described by CARA or CRRA

  7. Money measures of EU • Certainty equivalents are money values that can be derived from expected utility functions • In money units, CE’s measure the manager’s expected utility from a risky prospect • Mean-variance expected utility is a simple way to approximate CE’s

  8. Mean-variance (E-V) to express Expected Utility • Expected utility can be expressed as a function of mean and variance, i.e., • UEV(x) = xCE = E(x) – (/2)x2 • What is the risk premium () in this equation? • (/2) weights the variance • Alternative assumptions: • Manager has CARA utility and outcomes (x) follow normal distribution: x ~ N(x, x2) • Want local approximation to a generic expected utility function, using a Taylor series approximation

  9. Mean-Variance (EV) indifference curve and feasible set Source: Robison & Barry (1987)

  10. E-V risk programming models • Quadratic programming (QP) • Max E(x) subject to max Var(x) • Min Var(x) subject to min E(x) • Max E(x) – (/2)Var(x) • Minimization of Total Absolute Deviations (MOTAD) is analogous to QP but is linear (so uses LP algorithm)

  11. Other risk programming models • Extensions of sensitivity analysis • Breakeven values (parametric programming) • Catastrophic risk modeling • Safety-first programming • Chance-constrained programming

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