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A Simple Approach for Measuring Higher-Order Arrow-Pratt Coefficients of Risk Aversion

Author

Listed:
  • Cary Deck

    (Department of Economics, University of Alabama, Tuscaloosa, Alabama 35487; and Economic Science Institute, Chapman University, Chapman University, Orange, California 92866)

  • Rachel J. Huang

    (Department of Finance, National Central University, Taoyuan 32001, Taiwan)

  • Larry Y. Tzeng

    (Department of Finance, National Taiwan University, Taipei 10617, Taiwan)

  • Lin Zhao

    (School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China)

Abstract

Although the importance of the second-order Arrow-Pratt coefficients of risk aversion is well established, the importance of higher-order risk attitudes has only recently begun to be recognized. In this paper, we introduce a nonparametric approach to directly measure higher-order Arrow-Pratt coefficients of risk aversion in an expected utility framework using choices between compound lotteries and show how it can be easily implemented in behavioral studies. Specifically, we provide a theoretical basis for using risk apportionment to reveal the intensity of higher-order risk attitudes, and then draw upon our theoretical results to develop a simple, systematic, and generalizable procedure for eliciting higher-order Arrow-Pratt coefficients. We demonstrate our approach in a laboratory experiment and find that the modal second-order, third-order, and fourth-order Arrow-Pratt coefficients are positive and small. Further, we find that degrees of risk aversion are positively correlated across orders. Additionally, we discuss alternative implementations of our procedure.

Suggested Citation

  • Cary Deck & Rachel J. Huang & Larry Y. Tzeng & Lin Zhao, 2025. "A Simple Approach for Measuring Higher-Order Arrow-Pratt Coefficients of Risk Aversion," Management Science, INFORMS, vol. 71(8), pages 6979-6996, August.
    • Handle: RePEc:inm:ormnsc:v:71:y:2025:i:8:p:6979-6996
    DOI: 10.1287/mnsc.2023.02300
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