The multifactor asset pricing model derived from the Fama-French approach is extensively used in asset risk premium estimation procedures. Even including a considerable number of factors, it is still possible that omitted factors affect the estimation of this model. In this work, we compare...

A monopoly seller who faces ambiguity about the consumption plans of a buyer wishes to design a robust price menu that maximizes her guaranteed (worst-case) profit. We show that an optimal total-price menu can simply be linear in quantity under general conditions and that this qualitative result...

In this paper we address the problem of robust portfolio allocation under uncertainty with respect to the dependence between risky asset returns and for an ambiguity averse investor. We use the multiplier preferences framework with a penalty for ambiguity aversion that is proportional to the...

The availability of deep hedging has opened new horizons for solving hedging problems under a large variety of realistic market conditions. At the same time, any model – be it a traditional stochastic model or a market generator – is at best an approximation of market reality, prone to...

Building on automatic differentiation, I propose a solution method for heterogeneous agents models with many aggregate equations which allows to account even for strong nonlinearities. A powerful open source reference implementation is provided which typically solves the canonical HANK model...

Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model’s implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to...

We replicate the analysis conducted by Frederiksen, 2022a. We focus on assessing the computational and robustness replicability of their work. We find that their main exhibits and supplementary analysis are replicable, both when running their original Stata replication package, and when we...