We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

This paper uses the Extreme Value Theory (EVT) to model the rare events that appear as delivery delays in road transport. Transport delivery delays occur stochastically. Therefore, modeling such events should be done using appropriate tools due to the economic consequences of these extreme events. Additionally, we provide the estimates of the extremal index and the return level with the confidence interval to describe the clustering behavior of rare events in deliveries. The Generalized Extreme Value Distribution (GEV) parameters are estimated using the maximum likelihood method and the penalized maximum likelihood method for better small-sample properties. The findings demonstrate the advantages of EVT-based prediction and its readiness for application.

Purpose: This study analyses the determinants of economic growth acceleration in two completely different sets of institutions provided by two example economies, using a modified threshold error correction model (TECM). Design/Methodology/Approach: The contribution is based on an extended (sequential) methodology related to a set of two-regime threshold error correction models (TECM) that helps to investigate determinants of economic growth in both long run and short run. The validation of the results were prepared using Hansen and Seo test. Supportive role of structural breaks analysis is emphasized. Findings: There exists a set of variables that determine accelerated economic growth patterns. In the export-oriented Irish economy with a stable set of institutions and well educated society the growth has been led by net exports. In the case of Turkey, periods of accelerated growth appeared in short time so the results are much diversified, taking both factors and periods of growth. Practical Implications: It is possible to indicate a diversity of the growth patterns as well as evaluate the economic policy corresponding to them. Originality/Value: The study offers an in-depth insight into econometric modelling of economic growth as well as the fluctuations around it.