Theories of choice under risk: Insights from financial markets

Kliger, Doron; Levy, Ori

doi:10.1016/j.jebo.2009.01.012

Cited by 60 publications

(33 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kliger & Levy (2009) estimate various models for the period 1986-1995 and conclude that models that allow for probability weighting fit much better than EUT does. The second study, covering the period 1996-2008, also finds evidence for nonlinear probability weighting using a nonparametric approach (Polkovnichenko & Zhao 2009).…”

Section: Field Evidencementioning

confidence: 99%

Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences

2012

View full text Add to dashboard Cite

A large body of evidence has documented that risk preferences depend nonlinearly on outcome probabilities. We discuss the foundations and economic consequences of probability-dependent risk preferences and offer a practitioner's guide to understanding and modeling probability dependence. We argue that probability dependence provides a unifying framework for explaining many real-world phenomena, such as the equity premium puzzle, the long-shot bias in betting markets, and households' underdiversification and their willingness to buy small-scale insurance at exorbitant prices. Recent findings indicate that probability dependence is not just a feature of laboratory data, but is indeed manifest in financial, insurance, and betting markets. The neglect of probability dependence may prevent researchers from understanding and predicting important phenomena.

show abstract

Section: Field Evidencementioning

confidence: 99%

Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences

2012

View full text Add to dashboard Cite

show abstract

“…In this case, we use in the boxplot the average of the estimates of p in these two regions. 13 The only papers we are aware of in this regard are Kliger and Levy (2009) and Gurevich, Kliger, and Levy (2009). In these two papers, the authors assume the reference point of the representative agent therein to be status quo and the evaluation period to be one month.…”

Section: Calibrating Cpt Parameters λ and Pmentioning

confidence: 99%

Profit Sharing in Hedge Funds

Kou

2016

Mathematical Finance

View full text Add to dashboard Cite

In a new scheme for hedge fund managerial compensation known as the first‐loss scheme, a fund manager uses her investment in the fund to cover any fund losses first; by contrast, in the traditional scheme currently used in most US funds, the manager does not cover investors' losses in the fund. We propose a framework based on cumulative prospect theory to compute and compare the trading strategies, fund risk, and managers' and investors' utilities in these two schemes analytically. The model is calibrated to the historical attrition rates of US hedge funds. We find that with reasonable parameter values, both fund managers' and investors' utilities can be improved and fund risk can be reduced simultaneously by replacing the traditional scheme (with 10% internal capital and 20% performance fee) with a first‐loss scheme (with 10% first‐loss capital and 30% performance fee). When the performance fee in the first‐loss scheme is 40% (a current market practice), however, such substitution renders investors worse off.

show abstract

“…Observe that Barseghyan, Prince, and Teitelbaum (2011) and Einav et al (2012) treat stability as a testable hypothesis, whereas we treat stability as an identifying restriction. likelihood; Kliger and Levy (2009) use data on call options on the S&P 500 index to estimate a rank-dependent expected utility model and a cumulative prospect theory model by nonlinear least squares; Chiappori, Gandhi, Salanié, and Salanié (2012) use data on bets on U.S. horse races to estimate a non-expected utility model by nonparametric regression using generalized additive models (GAMs); 8 and Andrikogiannopoulou and Papakonstantinou (2013) use data on bets in an online sports book to estimate a cumulative prospect theory model by parametric Markov chain Monte Carlo (MCMC). 9 Andrikogiannopoulou and Papakonstantinou (2013) also estimate a mixture model of cumulative prospect theory that classifies bettors into preference types; however, they again estimate the model by parametric MCMC.…”

Section: Related Literaturementioning

confidence: 99%

Inference Under Stability of Risk Preferences

2014

View full text Add to dashboard Cite

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

show abstract

Theories of choice under risk: Insights from financial markets

Cited by 60 publications

References 59 publications

Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences

Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences

Profit Sharing in Hedge Funds

Inference Under Stability of Risk Preferences

Contact Info

Product

Resources

About