Methodologies such as situational importance and Shapley Additive explanations (SHAP) are increasingly used to explain machine learning (black-box) models predictions. Based on parametric inference, we develop a methodological framework that uses Shapley values to generalize the data-generating...

This paper develops a new econometric framework to estimate and classify exchange rate regimes. They are classified into four distinct categories: fixed exchange rates, BBC (band, basket and crawl), managed floating, and freely floating. The procedure captures the patterns of exchange rate...

We propose a novel methodology for modeling and forecasting multivariate realized volatilities using graph neural networks. This approach extends the work of Zhang et al. [2022] (Graph-based methods for forecasting realized covariances) and explicitly incorporates the spillover effects from...