Mixture models of choice under risk

Conte, Anna; Hey, John D.; Moffatt, Peter G.

doi:10.1016/j.jeconom.2009.10.011

Cited by 111 publications

(64 citation statements)

References 27 publications

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“…Of course, further research is needed to test whether similar conclusions hold for more general models of risk preferences beyond the reference EUT and CRRA cases considered here (Hey and Orme, 1994;Bruhin et al, 2010;Wakker, 2010); when risk preferences are structurally estimated jointly with time preferences and using mixtures models (Andersen et al, 2008b(Andersen et al, , 2014Harrison et al, 2009;Conte et al, 2011;Balcombe and Fraser, 2015;Galizzi et al, 2016b); or when the analysis account for the fact that respondents in a representative sample like ours integrate experimental prizes within individual-specific levels of background income (Andersen et al, 2015;Galizzi et al, 2016a Descriptive statistics Notes -Descriptive statistics computed using sampling weights. Standard errors adjusted to account for strata and PSU in the survey.…”

Section: Discussionmentioning

confidence: 92%

Temporal Stability, Cross-Validity, and External Validity of Risk Preferences Measures: Experimental Evidence from a UK Representative Sample

2016

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We conduct an "artefactual" field experiment to incorporate three different risk preferences measures within the Innovation Panel (IP) of the UK Household Longitudinal Survey (UKHLS). We randomly allocate to an experimental module a nationally representative sample of 661 adult respondents to the IP Wave 6 (IP6). These subjects respond to the incentive-compatible tasks by Holt and Laury (2002) (HL), and by Binswanger (1980Binswanger ( , 1981 and Eckel and Grossman (2008) (B-EG), and to the SOEP survey questions by Dohmen et al. (2011) for self-reported willingness to take risks in general (SOEP-G), in finance (SOEP-F), and in health (SOEP-H). One year later (IP7) the same measures are repeated for 413 of these respondents. This design allows us to systematically test, for a UK representative sample, the validity of the three measures along three dimensions. First, we look at cross-validity by testing how responses at one point in time correlate across the three tasks, assuming a Constant Relative Risk Aversion (CRRA) utility function. Second, we look at temporal stability by comparing the responses across IP6 and IP7. Third, we look at external validity by considering a range of risky health and financial behaviors in the UKHLS. We have three main findings. First, concerning cross-validity, we find evidence that the different measures generally correlate and map into each other, although their associations are not perfect. Second, concerning temporal stability, there are significant and positive correlations of the B-EG, HL, and SOEP measures across IP6 and IP7. Finally, we find mixed evidence concerning external validity.

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Section: Discussionmentioning

confidence: 92%

Temporal Stability, Cross-Validity, and External Validity of Risk Preferences Measures: Experimental Evidence from a UK Representative Sample

2016

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“…2). We chose this specific task because it is a well-validated means of gauging changes in risk preferences (25,26). More importantly, The acute plot shows average salivary cortisol levels on the first day of the study, before (day 0, S1) and 90 min after (day 0, S2) the first dose of hydrocortisone or placebo.…”

Section: Computerized Risk Tasks Show Greater Risk Aversion Under Chrmentioning

confidence: 99%

“…Lottery B is thus the risky bet, and lottery A the safe bet. the lotteries in this task enabled us to separate the participants' observed risk taking into two components: first, the way they valued the payouts of the lotteries (i.e., their utility functions) and, second, the way they weighted the probabilities of each payout when making their choices (i.e., their probability weighting functions) (26). We will consider each of these functions in turn.…”

Section: Figmentioning

confidence: 99%

Cortisol shifts financial risk preferences

Kandasamy

Hardy

Page

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

209

137

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Risk taking is central to human activity. Consequently, it lies at the focal point of behavioral sciences such as neuroscience, economics, and finance. Many influential models from these sciences assume that financial risk preferences form a stable trait. Is this assumption justified and, if not, what causes the appetite for risk to fluctuate? We have previously found that traders experience a sustained increase in the stress hormone cortisol when the amount of uncertainty, in the form of market volatility, increases. Here we ask whether these elevated cortisol levels shift risk preferences. Using a double-blind, placebo-controlled, cross-over protocol we raised cortisol levels in volunteers over 8 d to the same extent previously observed in traders. We then tested for the utility and probability weighting functions underlying their risk taking and found that participants became more risk-averse. We also observed that the weighting of probabilities became more distorted among men relative to women. These results suggest that risk preferences are highly dynamic. Specifically, the stress response calibrates risk taking to our circumstances, reducing it in times of prolonged uncertainty, such as a financial crisis. Physiology-induced shifts in risk preferences may thus be an underappreciated cause of market instability.

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“…10 Bruhin, Fehr-Duda, and Epper (2010) use experimental data (choices over binary money lotteries) to estimate a mixture model of cumulative prospect theory by parametric maximum likelihood. Conte, Hey, and Moffatt (2011) use similar experimental data to estimate a mixture model of rank-dependent expected utility theory by parametric maximum simulated likelihood.…”

Section: Related Literaturementioning

confidence: 99%

Inference Under Stability of Risk Preferences

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non-expected utility models, we perform a revealed preference analysis that yields household-specific bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households' risk preferences from the intervals defined by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best fits the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences. Terms of use: Documents in EconStor may

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Mixture models of choice under risk

Cited by 111 publications

References 27 publications

Temporal Stability, Cross-Validity, and External Validity of Risk Preferences Measures: Experimental Evidence from a UK Representative Sample

Temporal Stability, Cross-Validity, and External Validity of Risk Preferences Measures: Experimental Evidence from a UK Representative Sample

Cortisol shifts financial risk preferences

Inference Under Stability of Risk Preferences

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