| Abstract [eng] |
EU ALLOWANCES PRICE ANALYSIS AND DEPENDENCY ON VARIOUS VARIABLES. The means of production used in the industrial sector and their side effects, leading to significant emissions of CO2 and other greenhouse gases, have a strong impact on global warming. Environmental institutions seek to manage internal processes, to reduce anthropogenic pollution through various actions. The most relevant solution to the problem of pollution nowadays is producing 'cleaner' energy and reducing energy consumption overall. The main factor in implementing such a plan is the taxation of CO2 emissions. The thesis consists of four main parts: comprehensive analysis of literature and scientific research, its results, econometric analysis, conclusion and recommendations. Literature analysis identifies main events, that led to creating European Trading System, analyses scientific research, as well as summarizes reasoning behind picking main subject – price of European Union emission allowance (EUA). Main purpose of this master thesis is to analyze and investigate the price determination of the European Union emissions trading scheme (EU ETS) allowances, identifying best suitable model for future pricing forecast, as well as it‘s dependency on various variables. Theoretical findings were tested with empirical data from 2008 to 2019 in the EU ETS market. Dependent variable - quarterly prices of EUA. Main methods, selected for most precise identification of forecasting model: exponential smoothing, vector autoregression, autoregressive integrated moving average and generalized autoregressive conditional heteroskedastiveness models, also absolute percentage error prediction model. Regression analysis showed that European Union allowances (EUA) are most influenced by BRENT oil price as well as Stock Market Index (EURO STOXX 50). Other variables: natural gas, electricity and coal prices were insignificant regressors, due to that, it was inevitable to eliminate them from regression equation. Calculated outcome of all models led to a conclusion that EUA prices were best predicted by ARIMA (0,2,2)(0,0,1) and integrated GARCH model. However, after evaluating the Akaike criterion, the most accurate model was IGARCH(1,1) model for time series analysis, regarding estimation of European trading system allowances pricing. Performed analysis and calculations revealed that short-term price of EUA will probably experience only a slight change. Conclusion and recommendations summarize the main concepts of literature analysis as well as the results of the performed analysis. |
