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Equilibrium investment with random risk aversion.

Sascha Desmettre1, Mogens Steffensen2

  • 1Institute for Financial Mathematics and Applied Number Theory University of Linz Linz Austria.

Mathematical Finance
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Summary

Investors with random preferences can maximize utility using expected certainty equivalents. This approach addresses time-consistency issues, with equilibrium stock proportions independent of wealth but decreasing over time for power utility.

Keywords:
certainty equivalentsequilibrium approachpower and exponential utilityrandom risk aversiontime‐inconsistency

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Area of Science:

  • Finance
  • Decision Theory
  • Mathematical Economics

Background:

  • Traditional investment models assume stable investor preferences.
  • Random preferences introduce complexities in utility maximization and decision-making.
  • Time-consistency is a critical challenge in dynamic financial planning.

Purpose of the Study:

  • To formulate and solve the problem of utility maximization under random preferences.
  • To address and resolve time-consistency issues in dynamic decision-making.
  • To analyze the behavior of investment strategies for specific utility functions.

Main Methods:

  • Formulation of the investment problem using expected certainty equivalents.
  • Application of equilibrium theory to ensure time-consistency.
  • Development of rigorous definitions and a verification theorem.
  • Analytical calculations for power and exponential utility functions.

Main Results:

  • The proposed formulation effectively handles random preferences.
  • Equilibrium theory successfully resolves time-consistency challenges.
  • For power utility, equilibrium stock proportion is wealth-independent and time-decreasing.
  • For exponential utility, constant absolute risk aversion is replaced by its expectation.

Conclusions:

  • The expected certainty equivalent approach provides a robust framework for investors with random preferences.
  • Equilibrium theory is a valid method for ensuring time-consistent strategies in dynamic settings.
  • The findings offer insights into optimal portfolio allocation under uncertainty and changing preferences.