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In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

This paper reports a study on the causal dynamics between spot oil price, exchange rates, and stock prices in Poland, the Czech Republic, Hungary, Romania, and Serbia. The results are compared with a benchmark analysis in which U.S. monthly data are used, and time periods are selected according to the flexibility of exchange rate regimes in each country. A period between 2000 and 2015 is analyzed. The methodology is based on the Granger causality test, and the non-linear Diks-Panchenko test, while the causality in variance is checked with the Hafner-Herwartz test.

Abstract:The purpose of this research is the construction and analysis of a macroeconomic model of property prices in Poland covering the period between 2006 and 2013. The model is based on a supply-demand approach. Property prices have been found to be significantly affected by wages (positive correlation), unemployment rate (negative correlation), construction costs (positive correlation) and size of population (positive correlation). The panel data were collected from the biggest cities in voivodeships (Polish administrative regions). Both models with fixed and random effects were analyzed. Some similarities with a developed (UK) market have been found as well.