One of the common problems with the production of wind energy is the increased uncertainty surrounding the delivered output. Wind power producers adopt various strategies to reduce production risk, one of the most popular being the dispersion of wind farms across distant locations. The purpose...

We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time...

We apply linear and nonlinear panel models to investigate fundamentals pricing relationships in the Greek day-ahead power market. We link the persistence behaviour and other dynamic properties of power price trajectories to an extended array of fundamental variables reflecting market conditions...

This paper discusses applications of nature-inspired computational techniques in optimisation problems encountered in portfolio selection and applied econometrics. By means of an empirical study, we show how particle swarm intelligence can be effectively used in the estimation of a GARCH and an...

This paper proposes a hybrid computational intelligent system for the detection of statistical arbitrage opportunities in pairs of assets. The proposed methodology combines nonlinear neural network autoregressive models with GARCH parametrizations of volatility for describing the dynamics of the...

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind power traders ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial...