A kind of worst-case value-at-risk, GVaR, is defined to measure risk incorporating model uncertainty. Compared with most extant notions of worst-case VaR, GVaR can be computed by an explicit formula, and can be applied to large portfolios of several hundreds dimensions with low computational...

We identify the three types of risks involved in an art-secured lending operation and present a framework to assess their combined effects via a Monte Carlo simulation. Also, we derive some useful closed-form expressions that are suitable when the collateral consists of only one painting. To...

An extensive list of risks relative to big data frameworks and their use through models of artificial intelligence is provided along with measurements and implementable solutions. Bias, interpretability and ethics are studied in depth, with several interpretations from the point of view of...

We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision making process by using the power of spectral analysis. We use several key...

The catastrophic events are characterized by "low frequency and high severity". Nevertheless, during the last decades, both the frequency and the magnitude of these events have been significantly rising worldwide. In 2021, the European Commission adopted a new Strategy on Adaptation to Climate...

Banking systems are at the center of the financial infrastructure of any country. It has become apparent after the subprime crisis that such systems cannot be studied by looking at their components individually (that is, in isolation). Thus, an integrated approach is needed.In this paper we...

Within the finance literature there is an apparent gap between the inherent risk premium ignorance of a risk parity approach on the one hand and the assumed risk premium clairvoyance of a mean variance approach on the other. We propose a portfolio selection framework that allows an investor to...

Every model presents an approximation of reality and thus modeling inevitably implies model risk. We quantify model risk in a non-parametric way, i.e., in terms of the divergence from a so-called nominal model. Worst-case risk is defined as the maximal risk among all models within a given...

Default risk models have been widely employed to assess the ability of households and sovereigns to insure themselves against shocks. Grid search has often been used to solve these models because the complexity of the problem prevents the use of faster but less general methods. In this paper, we...

In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty...