This paper proposes a Markov-switching framework to endogenously identify periods where economies are more likely to:(i) synchronously enter recessionary and expansionary phases, and (ii) follow independent business cycles. The reliability of the framework is validated with simulated data in...

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...

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...

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an...

This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the...

This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and sentiment of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning...

We study and measure uncertainty in the minutes of the meetings of the board of governors of the Central Bank of Mexico and relate it to monetary policy variables. In particular, we construct two uncertainty indices for the Spanish version of the minutes using unsupervised machine learning...