The paper proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms. The model applies similarity networks to Shapley values, so that AI predictions are grouped...

The global financial crisis that began in 2007 has forced a re-examination of macroeconomics, financial economics, regulation, and risk management. Traditional macroeconomics overlooks the importance of risk which makes it ill-suited to analyze risk transmission, contagion and how risks can...

Technological development particularly boosted by artificial intelligence (AI) has substantial potential to transform many aspects of human lives and the way doing businesses. On the one side, it can offer opportunities, while on the other brings challenges and increases risks. Financial...

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions,...

Purpose - This work seeks to investigate post-crisis measures banks have adopted in a bid to manage liquidity risk. It is based on the fact that the financial liquidity market was greatly affected during the recent economic turmoil and financial meltdown. During the crisis, liquidity risk...

In this article, we outline some concepts relating to the use of stress testing in credit risk management. We begin by providing a simple taxonomy of stress scenarios and discussing the trade-offs that different approaches require for implementation. Our taxonomy is modeled after one that is...

The issue of model risk in default modeling has been known since inception of the Academic literature in the field. However, a rigorous treatment requires a description of all the possible models, and a measure of the distance between a single model and the alternatives, consistent with the...

We establish a relationship between the idiosyncratic risk of portfolios and a parsimonious group of market variables. Because we are able to summarize idiosyncratic risk with this small group of variables, we are able to design stress-tests that describe portfolio-specific risks as market...

Using the test of Granger-causality in tail of Hong et al. (2009), we define and construct Granger-causality tail risk networks between 33 systemically important banks (G-SIBs) and 36 sovereign bonds worldwide. Our purpose is to exploit the structure of the Granger-causality tail risk networks...

We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii)...