Deep learning is a type of machine learning that has attracted a lot of attention in recent years because to its incredible achievements in a variety of applications like pattern recognition, audio recognition, computer vision, and natural language processing. Deep learning approaches can also...

The identification of the business cycle turn points is a challenging task. In this context, the evolution of new data typologies as internet searches data strongly impacted the construction of approaches practical to forecast macroeconomic variables. This work emphasizes the significance of...

Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide robustness and reliability. Due to the growth...

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...

When datasets present long conditional tails on their response variables, algorithms based on Quantile Regression have been widely used to assess extreme quantile behaviors. Value at Risk (VaR) and Conditional Tail Expectation (CTE) allow the evaluation of extreme events to be easily...

This paper investigates the effects of temporal changes at the individual and the social level and their impact on cooperation in higher-order networks. To that end, a theoretical framework is proposed to explain the probability of cooperation as a function of temporal variation and neural...

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...

Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial...