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.
Prospect theory's loss aversion is often measured in the accept-reject task, in which participants accept or reject the chance of playing a series of gambles. The gambles are two-branch 50/50 gambles with varying gain and loss amounts (e.g., 50% chance of winning $20 and a 50% chance of losing $10). Prospect theory quantifies loss aversion by scaling losses up by a parameter λ. Here we show that λ suffers from extremely poor parameter recoverability in the accept-reject task. λ cannot be reliably estimated even for a simple version of prospect theory with linear probability weighting and value functions. λ cannot be reliably estimated even in impractically large experiments with participants subject to thousands of choices. The poor recoverability is driven by a trade-off between λ and the other model parameters. However, a measure derived from these parameters is extremely well recovered—and corresponds to estimating the area of gain-loss space in which people accept gambles. This area is equivalent to the number of gambles accepted in a given choice set. That is, simply counting accept decisions is extremely reliably recovered—but using prospect theory to make further use of exactly which gambles were accepted and which were rejected does not work.
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)
While previous research has revealed several reasons why humans often help each other even when they do not receive immediate benefits, we explore a simple nudge that might get more of those good deeds done: the “maybe favor”. We first show conceptually that, compared to a conventional favor, humans are more willing to grant a favor to a stranger on which they might eventually not have to make good. Furthermore, we conducted a series of fully incentivized experiments (total N = 3475) where participants could make actual donations to charity. Introducing a “maybe” into our donation proposals by randomly revoking some donations not only led to significant increases in donation rates but also increased the total amount of donations. That is, due to biased perceptions of costs and benefits combined with non-linear probability weighting, the donations we revoked due to the “maybe” were overcompensated by an increased overall willingness-to-donate.
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.
You cannot accurately estimate an individual’s loss aversion using an accept-reject task
