Statistical Methods for Forecasting and Risk Analysis

Dear Colleagues,

In recent years, the fast accumulation and growing availability of data and computational power have led to the spread of sophisticated statistical and machine learning models, together with artificial intelligence-based systems.

While these methods have gained great popularity in data science applications, due to their capability of giving rise to powerful predictions, they have an intrinsic black box nature, where input data are elaborated through complex processes without effective control and monitoring of the operational, reputational, and strategic risks emerging from potential biases in forecasting.

Therefore, the use of statistical methods to predict financial, default, funding loss, and diagnostic-related risks (among others) requires the main criteria supporting the predictions to be known for assessing the related severity and fostering ad hoc measures to reduce them in the presence of shocks.

This Special Issue aims to share recent research, stimulate discussion, and disclose new research directions regarding the use of statistical techniques and AI systems to address the challenges which arise in forecasting and risk analysis. A particular focus will be devoted to papers (theoretical, empirical, and review) drawing attention to the application of models, computational techniques in estimation, simulation, and prediction in risk analysis contributing to decision making in various contexts (banks, firms, industries, transports, environmental and healthy institutions, higher education institutions, etc.).

Prof. Marialuisa Restaino
Prof. Marcella Niglio
Prof. Emanuela Raffinetti
Guest Editors

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