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Journal of Risk and Uncertainty

, Volume 59, Issue 1, pp 51–83 | Cite as

An experimental test of the predictive power of dynamic ambiguity models

  • Konstantinos GeorgalosEmail author
Article
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Abstract

In this paper we report results from an economic experiment where we investigate the predictive performance of dynamic ambiguity models in the gains domain. Representing ambiguity with the aid of a transparent and non-manipulable device (a Bingo Blower) and using two-stage allocation questions, we gather data that allow us to estimate particular parametric forms of the various functionals and compare their relative performance in terms of out-of-sample fit. Our data show that a dynamic specification of Prospect Theory has the best predictive capacity, closely followed by Choquet Expected Utility, while multiple-prior theories can predict choice only for a very restricted subset of our subjects.

Keywords

Ambiguity Belief updating Dynamic ambiguity models Non-expected utility Experiment 

JEL Classifications

C91 D81 D83 D90 

Notes

Acknowledgments

I am grateful to Glenn Harrison, John Hey, Ivan Paya, Vitalie Spinu and Mike Tsionas for providing helpful comments. The author would like to thank the Editor of this journal W. Kip Viscusi and a referee for very helpful comments that led to significant improvements in both the analysis and the presentation. This research was funded by a Research and Impact Support Fund awarded by the Department of Economics at the University of York (RIS 39). The financial aid of the Greek Scholarships Foundation (IKY) is gratefully recognised. The usual disclaimer applies.

Supplementary material

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References

  1. Abdellaoui, M., Baillon, A., Placido, L., Wakker, P. (2011). The rich domain of uncertainty: Source functions and their experimental implementation. American Economic Review, 101, 695–723.CrossRefGoogle Scholar
  2. Ahn, D., Choi, S., Gale, D., Kariv, S. (2014). Estimating ambiguity aversion in a portfolio choice experiment. Quantitative Economics, 5, 195–223.CrossRefGoogle Scholar
  3. Al-Najjar, N., & Weistein, J. (2009). The ambiguity aversion literature: A critical assessment. Economics and Philosophy, 25, 249–284.CrossRefGoogle Scholar
  4. Andersen, S., Fountain, J., Harrison, W.G., Hole, A., Rutstrom, E. (2012). Inferring beliefs as subjectively imprecise probabilities. Theory and Decision, 73, 161–184.CrossRefGoogle Scholar
  5. Antoniou, C., Harrison, G., Lau, M., Read, D. (2015). Subjective Bayesian beliefs. Journal of Risk and Uncertainty, 50, 35–54.CrossRefGoogle Scholar
  6. Bade, S. (2015). Randomization devices and the elicitation of ambiguity-averse preferences. Journal of Economic Theory, 159, 221–235.CrossRefGoogle Scholar
  7. Baillon, A., & Bleichrodt, H. (2015). Testing ambiguity models through the measurement of probabilities for gains and losses. American Economic Journal: Microeconomics, 7, 77–100.Google Scholar
  8. Baillon, A., Halevy, Y., Li, C. (2015). Experimental elicitation of ambiguity attitude using the random incentive system. Microeconomics working papers, Vancouver School of Economics.Google Scholar
  9. Baillon, A., Bleichrodt, H, Keskin, U., L’Haridon, O., Li, C. (2018). Learning under ambiguity: An experiment using initial public offerings on a stock market. Management Science, 64, 2181–2198.CrossRefGoogle Scholar
  10. Balcombe, K., & Fraser, I. (2015). Parametric preference functionals under risk in the gain domain: A Bayesian analysis. Journal of Risk and Uncertainty, 50, 161–187.CrossRefGoogle Scholar
  11. Barberis, N. (2012). A model of casino gambling. Management Science, 58, 35–51.CrossRefGoogle Scholar
  12. Burghart, D., Epper, T., Fehr, E. (2015). The ambiguity triangle: Uncovering fundamental patterns of behavior under uncertainty. Discussion Paper 9150, IZA.Google Scholar
  13. Charness, G., & Gneezy, U. (2010). Portfolio choice and risk attitudes: An experiment. Economic Inquiry, 48, 133–146.CrossRefGoogle Scholar
  14. Chateauneuf, A., Eichberger, J., Grant, S. (2007). Choice under uncertainty with the best and worst in mind: Neo-additive capacities. Journal of Economic Theory, 137, 538–567.CrossRefGoogle Scholar
  15. Choi, S., Fisman, R., Gale, D., Kariv, S. (2007). Consistency and heterogeneity of individual behavior under uncertainty. American Economic Review, 97, 1921–1938.CrossRefGoogle Scholar
  16. Cohen, M., Gilboa, I., Schmeidler, D. (2000). An experimental study of updating ambiguous beliefs. Risk, Decision and Policy, 5(2), 123–133.CrossRefGoogle Scholar
  17. Corgnet, B., Kujal, P., Porter, D. (2013). Reaction to public information in markets: How much does ambiguity matter? Economic Journal, 123, 699–737.CrossRefGoogle Scholar
  18. Cubitt, R., van de Kuilen, G., Mukerji, S. (2014). Discriminating between models of ambiguity attitude: A qualitative test. Technical report 692. University of Oxford, Discussion Paper Series.Google Scholar
  19. De Filippis, R., Guarino, A., Jehiel, P., Kitagawa, T. (2016). Updating ambiguous beliefs in a social learning experiment. Technical Report CWP 18/16, cemmap working paper.Google Scholar
  20. Dempster, A.P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.CrossRefGoogle Scholar
  21. Dempster, A.P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological), 30, 205–247.CrossRefGoogle Scholar
  22. Dominiak, A., Dürsch, P., Lefort, J. (2012). A dynamic Ellsberg urn experiment. Games and Economic Behavior, 75, 625–638.CrossRefGoogle Scholar
  23. Easley, D., & O’Hara, M. (2009). Ambiguity and nonparticipation: The role of regulation. Review of Financial Studies, 22, 1817–1843.CrossRefGoogle Scholar
  24. Ebert, S., & Strack, P. (2015). Until the bitter end: On prospect theory in a dynamic context. American Economic Review, 105, 1618–33.CrossRefGoogle Scholar
  25. Eichberger, J., Grant, S., Kelsey, D. (2007). Updating Choquet beliefs. Journal of Mathematical Economics, 43, 888–899.CrossRefGoogle Scholar
  26. Eichberger, J., Grant, S., Kelsey, D. (2010). Comparing three ways to update Choquet beliefs. Economics Letters, 107, 91–94.CrossRefGoogle Scholar
  27. Ellsberg, D. (1961). Risk, ambiguity and the Savage axioms. Quarterly Journal of Economics, 75, 643–669.CrossRefGoogle Scholar
  28. Epstein, L., & Schneider, M. (2003). Recursive multiple-priors. Journal of Economic Theory, 113, 1–31.CrossRefGoogle Scholar
  29. Epstein, L., & Schneider, M. (2007). Learning under ambiguity. Review of Economic Studies, 74, 1275–1303.CrossRefGoogle Scholar
  30. Epstein, L., Noor, J., Sandroni, A. (2010). Non-Bayesian learning. The B.E. Journal of Theoretical Economics, 10, 1–20.Google Scholar
  31. Etner, J., Jeleva, M., Tallon, J.M. (2012). Decision theory under ambiguity. Journal of Economic Surveys, 26(2), 234–270.CrossRefGoogle Scholar
  32. Ferecatu, A., & Önçüler, A. (2016). Heterogeneous risk and time preferences. Journal of Risk and Uncertainty, 53, 1–28.CrossRefGoogle Scholar
  33. Gelman, A., & Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–472.CrossRefGoogle Scholar
  34. Georgalos, K. (2016). Dynamic decision making under ambiguity: A porfolio choice experiment. Working papers 2016/004, Lancaster University Management School, Economics Department.Google Scholar
  35. Ghirardato, P. (2002). Revisiting Savage in a conditional world. Journal of Economic Theory, 20, 83–92.CrossRefGoogle Scholar
  36. Ghirardato, P., Maccheroni, F., Marinacci, M. (2004). Differentiating ambiguity and ambiguity attitude. Journal of Economic Theory, 118, 133–173.CrossRefGoogle Scholar
  37. Gilboa, I., & Schmeidler, D. (1989). Maxmin expected utility with non-unique prior. Journal of Mathematical Economics, 18, 141–153.CrossRefGoogle Scholar
  38. Gilboa, I., & Schmeidler, D. (1993). Updating ambiguous beliefs. Journal of Economic Theory, 59, 33–49.CrossRefGoogle Scholar
  39. Gneezy, U., Imas, A., List, J. (2015). Estimating individual ambiguity aversion: A simple approach. Technical Report 20982, NBER Working Paper.Google Scholar
  40. Goldstein, W., & Einhorn, H. (1987). Expression theory and the preference reversal phenomena. Psychological Review, 236–254.Google Scholar
  41. Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with ORSEE. Journal of the Economic Science Association, 114–125.CrossRefGoogle Scholar
  42. Hanany, E., & Klibanoff, P. (2009). Updating ambiguity averse preferences. The B.E. Journal of Theoretical Economics, 9(1), 1–53.CrossRefGoogle Scholar
  43. Hayashi, T., & Wada, R. (2010). Choice with imprecise information: An experimental approach. Theory and Decision, 69, 355–373.CrossRefGoogle Scholar
  44. Hey, J. (2014). Chapter 14 - choice under uncertainty: Empirical methods and experimental results. In Machina, M., & Viscusi, W.K. (Eds.) Handbook of the economics of risk and uncertainty. (pp. 809–850). North-Holland.Google Scholar
  45. Hey, J., & Pace, N. (2014). The explanatory and predictive power of non two-stage-probability models of decision making under ambiguity. Journal of Risk and Uncertainty, 49, 1–29.CrossRefGoogle Scholar
  46. Hey, J., & Panaccione, L. (2011). Dynamic decision making: What do people do? Journal of Risk and Uncertainty, 42, 85–123.CrossRefGoogle Scholar
  47. Hey, J., Lotito, G., Maffioletti, A. (2010). The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity. Journal of Risk and Uncertainty, 41, 81–111.CrossRefGoogle Scholar
  48. Jeong, D., Kim, H., Park, J. (2015). Does ambiguity matter? Estimating asset pricing models with a multiple-priors recursive utility. Journal of Financial Economics, 115, 361–382.CrossRefGoogle Scholar
  49. Klibanoff, P., & Hanany, E. (2007). Updating preferences with multiple priors. Theoretical Economics, 2(3), 261–298.Google Scholar
  50. Klibanoff, P., Marinacci, M., Mukerji, S. (2005). A smooth model of decision making under ambiguity. Econometrica, 73, 1849–1892.CrossRefGoogle Scholar
  51. Klibanoff, P., Marinacci, M., Mukerji, S. (2009). Recursive smooth ambiguity preferences. Journal of Economic Theory, 144, 930–976.CrossRefGoogle Scholar
  52. Kothiyal, A., Spinu, V., Wakker, P. (2014). An experimental test of prospect theory for predicting choice under ambiguity. Journal of Risk and Uncertainty, 48, 1–17.CrossRefGoogle Scholar
  53. Li, W., Tiwari, A., Tong, L. (2017). Investment decisions under ambiguity: Evidence from mutual fund investor behavior. Management Science, 63, 2397–2771.CrossRefGoogle Scholar
  54. Loomes, G. (1991). Evidence of a new violation of the independence axiom. Journal of Risk and Uncertainty, 4, 91–108.CrossRefGoogle Scholar
  55. Loomes, G., & Pogrebna, G. (2014). Measuring individual risk attitudes when preferences are imprecise. The Economic Journal, 124, 569–593.CrossRefGoogle Scholar
  56. Machina, M., & Schmeidler, D. (1992). A more robust definition of subjective probability. Econometrica, 60, 745–80.CrossRefGoogle Scholar
  57. Machina, M., & Siniscalchi, M. (2014). Chapter 13 - ambiguity and ambiguity aversion. In Machina, M., & Viscusi, W.K. (Eds.) Handbook of the economics of risk and uncertainty. (pp. 729–807). North-Holland.Google Scholar
  58. Marinacci, M. (2002). Learning from ambiguous urns. Statistical Papers, 43, 145–151.CrossRefGoogle Scholar
  59. Mele, A., & Sangiorgi, F. (2015). Uncertainty, information acquisition, and price swings in asset markets. The Review of Economic Studies, 82, 1533–1567.CrossRefGoogle Scholar
  60. Oechssler, J., & Roomets, A. (2014). Unintended hedging in ambiguity experiments. Economics Letters, 122, 243–246.CrossRefGoogle Scholar
  61. Peysakhovich, A., & Karmarkar, U. (2016). Asymmetric effects of favorable and unfavorable information on decision making under ambiguity. Management Science, 62, 2163–2178.CrossRefGoogle Scholar
  62. Pires, C. (2002). A rule for updating ambiguous beliefs. Theory and Decision, 53, 137–152.CrossRefGoogle Scholar
  63. Plummer, M. (2017). JAGS Version 4.3.0 User Manual. Technical report.Google Scholar
  64. Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–527.CrossRefGoogle Scholar
  65. Savage, L. (1954). The foundations of statistics. New York: Wiley.Google Scholar
  66. Schmeidler, D. (1989). Subjective probability and expected utility without additivity. Econometrica, 57, 571–587.CrossRefGoogle Scholar
  67. Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.Google Scholar
  68. Siniscalchi, M. (2011). Dynamic choice under ambiguity. Theoretical Economics, 6, 379–421.CrossRefGoogle Scholar
  69. Stomper, A., & Vierø, M. (2015). Iterated expectations under rank-dependent expected utility and model consistency. Working Papers 1228, Queen’s University, Department of Economics.Google Scholar
  70. Stott, H. (2006). Cumulative prospect theory’s functional menagerie. Journal of Risk and Uncertainty, 32, 101–130.CrossRefGoogle Scholar
  71. Thimme, J., & Völkert, C. (2015). Ambiguity in the cross-section of expected returns: An empirical assessment. Journal of Business & Economic Statistics, 33, 418–429.CrossRefGoogle Scholar
  72. Trautmann, S., & van de Kuilen, G. (2015). Wiley blackwell handbook of judgment and decision making, chapter ambiguity attitudes (pp. 89–116). Blackwell.Google Scholar
  73. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.CrossRefGoogle Scholar
  74. Wakker, P. (2008). Explaining the characteristics of the power (CRRA) utility family. Health Economics, 17, 1329–1344.CrossRefGoogle Scholar
  75. Wakker, P. (2010). Prospect theory. Cambridge University Press.Google Scholar
  76. Wang, T. (2003). Conditional preferences and updating. Journal of Economic Theory, 286–321.CrossRefGoogle Scholar
  77. Wilcox, N. (2007). Predicting risky choices out-of-context: A Monte Carlo study. Technical report, University of Houston.Google Scholar
  78. Wilcox, N. (2008). Stochastic models for binary discrete choice under risk: A critical primer and econometric comparison. In Cox, J., & Harrison, G. (Eds.) Research in experimental economics, (Vol. 12 pp. 41–196): Emerald Group Publishing Limited.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EconomicsLancaster University Management SchoolLancasterUK

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