We show that if an agent is uncertain about the precise form of his utility function, his actual relative risk aversion may depend on wealth even if he knows his utility function lies in the class of constant relative risk aversion (CRRA) utility functions. We illustrate the consequences of this...

US data and new stockholding data from fifteen European countries and China exhibit a common pattern: stockholding shares increase in household income and wealth. Yet, there is a multitude of numbers to match through models. Using a single utility function across households (parsimony), we...

We propose a new decision criterion under risk in which people extract both utility from anticipatory feelings ex ante and disutility from disappointment ex post. The decision maker chooses his degree of optimism, given that more optimism raises both the utility of ex ante feelings and the risk...

This paper investigates whether preference interactions can explain why risk preferences change over time and across contexts. We conduct an experiment in which subjects accept or reject gambles involving real money gains and losses. We introduce within-subject variation by alternating...

We analytically show that a common across rich/poor individuals Stone-Geary utility function with subsistence consumption in the context of a simple two-asset portfolio-choice model is capable of qualitatively and quantitatively explaining: (i) the higher saving rates of the rich, (ii) the...

Assumptions about the dynamic and distributional behavior of risk factors are crucial for the construction of optimal portfolios and for risk assessment. Although asset returns are generally characterized by conditionally varying volatilities and fat tails, the normal distribution with constant...

Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail-correlation matrices based on Value-at-Risk (VaR) estimates. We demonstrate how to obtain more effi cient...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...