We investigate the pricing of temperature derivatives under weather forecasts modeled by enlarged filtrations. We also treat option pricing and optimal portfolio selection in temperature markets with future information. We finally prove an anticipative sufficient stochastic minimum principle and...

This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for...

Leadership behaviors of wildland firefighting teams engaged in firefighting activities are “shared” despite the existence of a highly bureaucratic and hierarchical organizational structure. This paradoxical type of leadership appears to be based on individual perceptions of the effectiveness...

There are many alternative approaches to selecting mortality models and forecasting mortality. The standard practice is to produce forecasts using a single model such as the Lee-Carter, the Cairns-Blake-Dowd, or the Age- Period-Cohort model, with model selection based on in-sample goodness of...

Outliers are observations that deviate significantly from the norm, and their detection has been a critical topic in various research areas and application domains, such as video surveillance, network intrusion detection, and disease outbreak detection. In recent years, deep learning-based...

In this paper, we develop a new model-based method to inference on totals and averages of nite populations segmented in planned domains or strata. Within each stratum, we decompose the total as the sum of its sampled and unsampled parts, making inference on the unsampled part using Bayesian...

As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter provides a review of popular estimation...

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of...

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price...

This paper considers sampling proportional to expected size from a partly unknown distribution. The applied context is the exploration for undiscovered resources, like oil accumulations in different deposits, where the most promising deposits are likely to be drilled first, based on some...