This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...

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...

Crucial for asset allocation and portfolio management is the estimation of volatility and covariance structure of asset returns. Even the simplest Markowitz portfolio construction requires estimates of the mean returns, variance and covariances. Various studies show that the calculation of...

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...

Electricity prices are known to have special statistical properties, where long-memory effects, varying-level periodicities, cross-dependencies and other exogenous influences often work together to form a complex data-generating process. The need for accurately representing all these features...

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...