The ability to accurately forecast the spot price of natural gas benefits stakeholders and is a valuable tool for all market participants in the competitive gas market. In this paper, we attempt to forecast the natural gas spot price 1, 3, 5, and 10 days ahead using machine learning methods: support vector machines (SVM), regression trees, linear regression, Gaussian process regression (GPR), and ensemble of trees. These models are trained with a set of 21 explanatory variables in a 5-fold cross-validation scheme with 90% of the dataset used for training and the remaining 10% used for testing the out-of-sample generalization ability. The results show that these machine learning methods all have different forecasting accuracy for every time frame when it comes to forecasting natural gas spot prices. However, the bagged trees (belonging to the ensemble of trees method) and the linear SVM models have superior forecasting performance compared to the rest of the models.

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk.