Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function, requiring the use of simulation-based inference,...

The resilience of supply networks to catastrophic events has received increased prominence following the effects on global supply of intentional and natural hazards, such as 9/11 and the Japanese tsunami respectively. We examine how supply network resilience relates to, for example, reliability,...

In the brain sciences, an influential paradigm is that the brain is a Bayesian prediction engine operating under a resource constraint. We study the implications of this view for option pricing. We show that this brain-based view leads to higher no-arbitrage prices than what the traditional...

This paper shows that past disclosure decisions cause the current disclosure decision using a Bayesian hierarchical model that flexibly accounts for firm economic characteristics that may explain persistence in disclosure decisions. A management forecast in the prior quarter, relative to its...

This letter develops a decision criterion which takes into account parameter and model uncertainty in an optimal portfolio choice problem. This criterion is a special case of the Variational Preferences, rewritten in the Bayesian statistics notations, which specifically exploits the information...

Liquidity issues have been increasingly addressed recently, especially with regards to optimal execution of large orders. In practice, agents are uncertain about the future dynamics of the assets, and face the risk of model misspecification, which could make the execution strategies non optimal....