Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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A Noise-Reduced Risk Aversion Index

  • Received : 2018.03.12
  • Accepted : 2018.03.25
  • Published : 2018.03.31
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Abstract

We propose a noise reduced risk aversion index for measuring risk aversion through a laboratory experiment to overcome disadvantages of the multiple pricing list format developed by Holt and Laury (2002). We use randomized multiple list choices with coarser classification and reward weighting, supplement the rank of risk aversion with extra individual characteristics of risk attitude, and construct an index of risk aversion by standardizing the risk aversion ranking with quantile normalization. Our method reduces multiple switching problems that noisy decision makers mistakenly commit in experimental approaches, so that it is free of the framing effect which severely occurred in the HL. Furthermore, the index doesn't utilize any specific utility function or probability weighting, which allows researcher to hold the independence axiom. Since our noise reduced index of risk aversion has many good traits, it is widely used and applied to reveal fundamental characteristics of risk-related behaviors in economics and finance regardless of experimental environment.

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References

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