The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of t...

Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. optimization is an iterative method where Gaussian process posterior underlying function sequentially updated new evaluations. We propose novel framework for situations user controls computational effort and therefore precision This common in econometrics likelihood computed Markov cha...

US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying parameters and stochastic volatility, which treats the nature of parameter as unknown. Coefficients can evolve according random walk, Markov switching process, observed predictors, or depend on mixture these. To decide fo...

Causality graphs are routinely estimated in social sciences, natural and engineering due to their capacity efficiently represent the spatiotemporal structure of multi-variate data sets a format amenable for human interpretation, forecasting, anomaly detection. A popular approach mathematically formalize causality is based on vector autoregressive (VAR) models constitutes an alternative well-kno...

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem model uncertainty is assessed explicitly by concentrating on specification selection based quality short-term out-of-sample forecasts (1 12 months ahead) for price wheat, soybeans and corn. Univariate multivariate autoregressiv...