infonet-economy

Structural models for forward electricity prices are of great relevance nowadays, given the major structural changes in the market due to the increase of renewable energy in the production mix. In this study, we aim at understanding the dynamics of the risk premium (the drift in the dynamics) and the noise (non-Gaussian, stochastic volatility) in futures prices for electricity. We…

At first glance the goals of model selection might seem clear. Out of a set of possible models, we want to select the ”best” or a subset of ”best” models. This notion of ”best” however is not well defined, since it obviously depends on the initial goals of the selection. In order to study the uncertainty in model selection, we introduce a new definition of a model, where the models…

This thesis contributes to the development of measures of model selection and model adequacy for mixed-effects models. In the context of linear mixed-effects models, we review and compare in a simulation study a large set of measures proposed to evaluate model adequacy or/and to perform model selection. In the more general context of generalized linear mixed-effects models, we…

Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and,…

Typical characteristics of electricity day-ahead prices at EPEX are the very high volatility and a large number of extreme price changes. In this paper, we look at hourly spot prices at the German electricity market and apply extreme value theory (EVT) to investigate the tails of the price change distribution. Our results show the importance of delimiting price spikes and modeling…

This PhD thesis concerns the modelling of time-varying causal relationships between two signals, with a focus on signals measuring neural activities. The ability to compute a dynamic and frequency-specific causality statistic in this context is essential and Granger causality provides a natural statistical tool. In Chapter 1 we propose a review of the existing methods allowing one to…

Nowadays, the increase in data size and model complexity has led to increasingly difficult estimation problems. The numerical aspects of the estimation procedure can indeed be very challenging. To solve these estimation problems, approximate methods such as pseudo-likelihood functions or approximated estimating equations can be used as these methods are typically easier to implement…

This paper introduces multivariate dynamic copula models to account for the time-varying dependence structure in asset portfolios. We firstly enhance the fexibility of this structure by modeling regimes with multivariate mixture copulas. In our second approach, we derive dynamic elliptical copulas by applying the dynamic conditional correlation model (DCC) to multivariate elliptical…