Cumulative Prospect Theory, Option Returns, and the Variance Premium

2018 ◽  
Vol 32 (9) ◽  
pp. 3667-3723 ◽  
Author(s):  
Lieven Baele ◽  
Joost Driessen ◽  
Sebastian Ebert ◽  
Juan M Londono ◽  
Oliver G Spalt

Abstract We develop a tractable equilibrium asset pricing model with cumulative prospect theory (CPT) preferences. Using GMM on a sample of U.S. equity index option returns, we show that by introducing a single common probability weighting parameter for both tails of the return distribution, the CPT model can simultaneously generate the otherwise puzzlingly low returns on both out-of-the-money put and out-of-the-money call options as well as the high observed variance premium. In a dynamic setting, probability weighting and time-varying equity return volatility combine to match the observed time-series pattern of the variance premium. Received May 30, 2017; editorial decision August 10, 2018 by Editor Andrew Karolyi.

2008 ◽  
Vol 98 (5) ◽  
pp. 2066-2100 ◽  
Author(s):  
Nicholas Barberis ◽  
Ming Huang

We study the asset pricing implications of Tversky and Kahneman's (1992) cumulative prospect theory, with a particular focus on its probability weighting component. Our main result, derived from a novel equilibrium with nonunique global optima, is that, in contrast to the prediction of a standard expected utility model, a security's own skewness can be priced: a positively skewed security can be “overpriced” and can earn a negative average excess return. We argue that our analysis offers a unifying way of thinking about a number of seemingly unrelated financial phenomena. (JEL D81, G11, G12)


Econometrica ◽  
2020 ◽  
Vol 88 (4) ◽  
pp. 1363-1409
Author(s):  
B. Douglas Bernheim ◽  
Charles Sprenger

Cumulative Prospect Theory (CPT), the leading behavioral account of decisionmaking under uncertainty, avoids the dominance violations implicit in Prospect Theory (PT) by assuming that the probability weight applied to a given outcome depends on its ranking. We devise a simple and direct nonparametric method for measuring the change in relative probability weights resulting from a change in payoff ranks. We find no evidence that these weights are even modestly sensitive to ranks. Conventional calibrations of CPT preferences imply that the percentage change in probability weights should be an order of magnitude larger than we observe. It follows either that probability weighting is not rank‐dependent, or that the weighting function is nearly linear. Nonparametric measurement of the change in relative probability weights resulting from changes in probabilities rules out the second possibility. Additional tests nevertheless indicate that the dominance patterns predicted by PT do not arise. We reconcile these findings by positing a form of complexity aversion that generalizes the well‐known certainty effect.


CFA Digest ◽  
2014 ◽  
Vol 44 (6) ◽  
Author(s):  
Brindha Gunasingham
Keyword(s):  

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