Year: 2019
Type: article
Abstract: Currency crises are major events in the international monetary system. They affect the monetary policy of countries and are associated with risks of vulnerability for open economies. Much research has been carried out on the behavior of these events, and models have been developed to predict falls in the value of currencies. However, the limitations of existing models mean further research is required in this area, since the models are still of limited accuracy and have only been developed for emerging countries. This article presents an innovative global model for predicting currency crises. The analysis is geographically differentiated for regions, considering both emerging and developed countries and can accurately estimate future scenarios for currency crises at the global level. It uses a sample of 162 countries making it possible to account for the regional heterogeneity of the warning indicators. The method used was deep neural decision trees (DNDTs), a technique based on decision trees implemented by deep learning neural networks, which was compared with other methodologies widely applied in prediction. Our model has significant potential for the adaptation of macroeconomic policy to the risks derived from falls in the value of currencies, providing tools that help ensure financial stability at the global level.
Source: Applied Sciences
DOI: 10.3390/app9235227
Institution: Universidad de Málaga
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,520
Funder: Universidad de Málaga
OpenAlex ID: W2993907965