We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify...

We show how to execute a macro-hedge on a portfolio that depends on several risk factors in a one period static context. We show, by applying a orthogonalization procedure, that adding a hedging instrument with just one underlying reduces the risk of the portfolio along several dimensions but up...

Financial networks' study and understanding has become extremely important since the global financial meltdown in 2007-2009 when the inter-connectedness of institutions has surfaced as one of the major culprits for the magnitude of the distress. This paper aims at providing a new approach, based...

Missing data is a problem appearing ubiquitously across many fields and needs to be dealt with systematically. For multivariate time series data imputation can be a challenging problem. We consider the particular case of credit default swap time series, where missing data can pose a considerable...

The purpose of this paper is to show a novel approach to automatically generating Probabilistic Causal Models (Bayesian Networks (BN)) by applying Natural Language Processing (NLP) techniques to a corpus of millions of digitally published news articles in which views by different authors are...

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around...