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Subjective Beliefs and Statistical Forecasts of Financial Risks: The Chief Risk Officer Project

In: Contemporary Challenges in Risk Management

Author

Listed:
  • Glenn W. Harrison
  • Richard D. Phillips

Abstract

Information about financial risks comes from many sources. We formally consider how one can elicit and use information from two important sources when making forecasts. One source is a traditional statistical forecast, using familiar econometric methods for extrapolating from the past to the future. The other source is the elicited subjective belief distributions of “experts” in this domain: Chief Risk Officers of major international corporations. We demonstrate how these beliefs can be elicited in a formal, structured and incentivized manner, and critically contain information on the precision of the individual’s belief for each risk. We characterize the manner in which these two sources tell different stories about these risks, arguing that any distributional differences, or similarities, between the two sources are informative for risk managers. Finally, we characterize the degree of consistency among our experts: are they “on the same page” in their beliefs? We argue, again, that consistency or inconsistency of subjective beliefs is in itself informative for risk managers.1

Suggested Citation

  • Glenn W. Harrison & Richard D. Phillips, 2014. "Subjective Beliefs and Statistical Forecasts of Financial Risks: The Chief Risk Officer Project," Palgrave Macmillan Books, in: Torben Juul Andersen (ed.), Contemporary Challenges in Risk Management, chapter 7, pages 163-202, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-1-137-44762-3_8
    DOI: 10.1057/9781137447623_8
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    Citations

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    Cited by:

    1. Aycinena, Diego & Bogliacino, Francesco & Kimbrough, Erik O., 2024. "Measuring norms: Assessing the threat of social desirability bias to the Bicchieri and Xiao elicitation method," Journal of Economic Behavior & Organization, Elsevier, vol. 222(C), pages 225-239.
    2. Zahra Murad & Martin Sefton & Chris Starmer, 2016. "How do risk attitudes affect measured confidence?," Journal of Risk and Uncertainty, Springer, vol. 52(1), pages 21-46, February.
    3. Marshall, Alasdair & Ojiako, Udechukwu & Wang, Victoria & Lin, Fenfang & Chipulu, Maxwell, 2019. "Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation," International Journal of Forecasting, Elsevier, vol. 35(2), pages 644-658.
    4. Christopher P. Chambers & Nicolas S. Lambert, 2021. "Dynamic Belief Elicitation," Econometrica, Econometric Society, vol. 89(1), pages 375-414, January.
    5. Ingrid Burfurd & Tom Wilkening, 2022. "Cognitive heterogeneity and complex belief elicitation," Experimental Economics, Springer;Economic Science Association, vol. 25(2), pages 557-592, April.
    6. David Danz & Lise Vesterlund & Alistair J. Wilson, 2020. "Belief Elicitation: Limiting Truth Telling with Information on Incentives," NBER Working Papers 27327, National Bureau of Economic Research, Inc.
    7. de Haan, Thomas, 2020. "Eliciting belief distributions using a random two-level partitioning of the state space," Working Papers in Economics 1/20, University of Bergen, Department of Economics.

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