This article introduces a very flexible framework for causal and predictive market views and stress-testing. The framework elegantly combines Bayesian networks (BNs) and Entropy Pooling (EP). In the new framework, BNs are used to generate a finite set of joint causal views / stress-tests for the...

This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is usually...

Wilfried Despagne describes a Web-based forecasting support system, “Horizons,” that was designed for a French subsidiary of a European temperature-controlled-transport group. The major challenge was the adaptation of the system to more than 70 different transportation hubs, requiring a...

In this study we propose a stochastic mortality forecast model that may be viewed as a Lévy process. First, age, period and cohort effects are objectively identified in a given matrix of historic mortality data. Next, these patterns are removed from the matrix of mortality improvement rates. We...

In modern conditions of instability, systematic crises and global transformations the problem of developing methods and technologies for analysis, modeling, management, forecasting and decision making for stable development of viable socioeconomic systems has become the most important. These...

Purpose – The recent growth of South Korean products in the international market is the benchmark for both developed as well as developing countries. According to the development index, the role of international trade is indeed crucial for the development of the national economy. However, the...

This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning...

This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows...

This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on...