Cost-efficient Payoffs under Model Ambiguity

TL;DR

This paper extends the concept of cost-efficient payoffs to settings with model ambiguity, proposing methods to find robust payoffs that minimize costs under worst-case distribution scenarios, and linking these to maxmin utility models.

Contribution

It introduces the concept of robust cost-efficient payoffs under ambiguity, connecting them with maxmin expected utility and providing solutions under various uncertainty conditions.

Findings

Robust cost-efficient payoffs are necessarily solutions to maxmin expected utility.

Solutions are characterized under conditions involving drift and volatility uncertainty.

The study bridges cost-efficiency with robust preference models in ambiguous settings.

Abstract

Dybvig (1988a,b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution ("cost-efficient payoff"). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution ("robust cost-efficient payoff") and determine solutions under certain conditions. We study the link between "robust cost-efficiency" and the maxmin expected utility setting of Gilboa and Schmeidler, as well as more generally with robust preferences in a possibly non-expected utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset.