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Dynamic Portfolio Selection under Uncertainty – Theory and Its Applications to R&D valuation

Dynamic Portfolio Selection under Uncertainty – Theory and Its Applications to R&D valuation. Janne Gustafsson Systems Analysis Laboratory. Mean-Risk Utility Theory. Status of Doctoral Studies. 1 article published in a conference proceedings PRIME Decisions 2 articles in review

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Dynamic Portfolio Selection under Uncertainty – Theory and Its Applications to R&D valuation

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  1. Dynamic Portfolio Selection under Uncertainty –Theory and Its Applications to R&D valuation Janne Gustafsson Systems Analysis Laboratory Mean-Risk Utility Theory

  2. Status of Doctoral Studies • 1 article published in a conference proceedings • PRIME Decisions • 2 articles in review • Contingent Portfolio Programming (CPP) • Mean-Risk Utility Theory • closely related to CPP’s objective function • 2 manuscripts under work • Case study on R&D project selection and real options valuation • Dynamic choice under risk • further work on CPP’s objective function • Visit to London Business School in January-April 2003

  3. Mean-Risk Utility Theory – Problem • Maurice Allais: DM should consider the entire probability distribution of (Jevonsian) utility • Actual outcomes of lotteries are irrelevant, because they do not reflect desirability • Risk must be related to the dispersion of utility • Expected Utility Theory: DM considers expectation of utility only • Based on Independence Axiom • Why do we need this axiom? • There are also other as appealing axioms as independence (e.g., betweenness) • Contradiction? • Concepts of utility different? • Does a von Neumann-Morgenstern utility function account for dispersion of Jevonsian utilities? • Are there any more general implications?

  4. Earlier Approahces • Independence Axiom has been challenged • Allais (1953) first by critisizing the sole use of expectation • Empirical studies later showed several violations of EUT • Result: Several non-expected utility theories • Allais (1953): Positive Theory • Kahneman and Tversky (1979): Prospect Theory • MacCrimmon and Chew (1979): Weighted Utility Theory • Quiggin (1982): Rank-dependent Expected Utility Theory • Machina (1982): Generalized Expected Utility Analysis • Yaari (1987): Dual Theory • Chew, Epstein, and Segal (1991): Quadratic Utility Theory • Choquet expected utility models, and many more... • Yet, rarely used • most are mathematically challenging • a part of the axioms are typically unintuitive

  5. Aim and Results • Aim: To show that risk attitude is related to dispersion of utilities • Cannot by accomplished by using EUT => Need for new approach • Use of several new techniques • e.g., preferences over consequences in the analysis of preferences over lotteries • A set of 5 assumptions / axioms • Preference model: • CE is the DM’s certainty equivalent operator • some real-valued functional that is consistent with stochastic dominance • e.g., CE[X] = E[X] – λ·LSAD[X] • u is a measurable (Jevonsian) utility function • based on algebraic or positive difference structure • Under EUT:

  6. Manuscript and Publication • Manuscript was written alone, but there were helpful discussions with various persons at SAL • Quite long; some 64 pages • extensive comparison to various approaches to choice under risk • detailed motivation of the assumptions made • Manuscript submitted to an economic journal in August 2002 • other authors had published many articles on the subject there • seemed to be the most appropriate publishing forum, should the theory prove correct • no decision made to date

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