In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression...

Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex...

One of the biggest challenges for the application of machine learning (ML) models in finance is how to explain their results. In recent years, different interpretability techniques have appeared to assist in this task, although their usefulness is still a matter of debate. In this article we...

In this paper we estimate inflation expectations for several Latin American countries using an affine model that takes as factors the observed inflation and the parameters generated from zero-coupon yield curves of nominal bonds. By implementing this approach, we avoid the use of...

In this paper we propose an affine model that uses as observed factors the Nelson and Siegel (NS) components summarising the term structure of interest rates. By doing so, we are able to reformulate the Diebold and Li (2006) approach to forecast the yield curve in a way that allows us to...

So-called cryptocurrencies are becoming more popular by the day, with a total market capitalization that exceeded $3 trillion at its peak in 2021. Bitcoin has emerged as the most popular among them, with a total valuation that reached an all-time high of $68,000 in November 2021. However, its...