We study the diffusion of an idea, a product, a disease, a cultural fad, or a technology among agents in a social network that exhibits segregation or homophily (the tendency of agents to associate with others similar to themselves). Individuals are distinguished by their types - e.g., race,...

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...

By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents' expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those...

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...

The main idea of this paper is to embed a classical actuarial regression model into a neural network architecture. This nesting allows us to learn model structure beyond the classical actuarial regression model if we use as starting point of the neural network calibration exactly the classical...

The Lee-Carter model is a basic approach to forecasting mortality rates of a single population. Although extensions of the Lee-Carter model to forecasting rates for multiple populations have recently been proposed, the structure of these extended models is hard to justify and the models are...

We present an actuarial loss reserving technique that takes into account both claim counts and claim amounts. Separate (over-dispersed) Poisson models for the claim counts and the claim amounts are combined by a joint embedding into a neural network architecture. As starting point of the neural...

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...

This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit...

The aim of this manuscript is to provide the mathematical and statistical foundations of actuarial learning. This is key to most actuarial tasks like insurance pricing, product development, claims reserving and risk management. The basic approach to these tasks is regression modeling. This...