When can expected utility handle first-order risk aversion?

Georges DIONNE, Jingyuan LI

Research output: Journal PublicationsJournal Article (refereed)peer-review

9 Citations (Scopus)

Abstract

Expected utility functions are limited to second-order (conditional) risk aversion, while non-expected utility functions can exhibit either first-order or second-order (conditional) risk aversion. We extend the concept of orders of conditional risk aversion to orders of conditional dependent risk aversion. We show that first-order conditional dependent risk aversion is consistent with the framework of the expected utility hypothesis. Our theoretical result proposes new insights into economic and financial applications such as the equity premium puzzle, the cost of business cycles, and stock market participation. Our model is compared to the rank-dependent expected utility model.
Original languageEnglish
Pages (from-to)403-422
Number of pages20
JournalJournal of Economic Theory
Volume154
Early online date30 Sept 2014
DOIs
Publication statusPublished - Nov 2014

Funding

This paper was presented at numerous conferences including “Risk and Choice: A conference in honor of Louis Eeckhoudt” in the Toulouse School of Economics, 2012. Both authors are grateful to two referees and an Associate Editor for comments that greatly improved the previous versions of the document. Georges Dionne acknowledges the financial support from the SSHRC in Canada (Grant 435-2012-1503). Jingyuan Li acknowledges the financial support from the Faculty Research Grant of Lingnan University under Research Project No. DR12A9 and Direct Grant for Research of Lingnan University under Research Project No. DR13C8.

Keywords

  • Background risk
  • Consumption risk in business cycles
  • Equity premium puzzle
  • Expected utility theory
  • First-order conditional dependent risk aversion
  • Rank-dependent expected utility model

Fingerprint

Dive into the research topics of 'When can expected utility handle first-order risk aversion?'. Together they form a unique fingerprint.

Cite this