The paper examines the primary drivers and factors influencing the volatility of natural gas prices in the world from January 2007 to July 2020. In addition to the narrow dependence between crude oil and natural gas prices, the influence of renewable energy production and coal production on the price of natural gas has been studied. For that purpose, the method of multiple linear regression was used. The results show that the volatility of natural gas prices significantly depends on the type of the shock in the natural gas market and that the total production of energy from renewable sources, production of coal and natural gas and the price of crude oil have a significant impact on the price of gas.

Tourism, as one of the main driving forces of economic development, is exposed to many risks. Besides frequent fluctuations in foreign currency exchange, prices of fuel and transportation, the tourism industry has become more sensitive to weather conditions lately. One of the new instruments which can be efficiently used for weather risk hedging is weather derivatives (forwards, futures, options and swaps on chosen weather variables - temperature, rain, snow, wind etc.). In this paper, we will present the possibility of weather derivatives application in winter tourism - snowfall forwards - in order to hedge the business of ski lift operator company. Our research is based on snowfall data of Kopaonik mountain ski resort and revenues of ski lift operator company. We will show that weather derivatives might be an effective tool for hedging weather risk and reducing the volatility of companies' revenues in the winter ski tourism business in Serbia.

In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.

The main goal of this work is to identify and analyze the impact of the Chinese economy key determinants on the global market copper price movements. Our research is based on a multilinear regression analysis of the historical data of the time period from 2003 until 2017. The presented multilinear regression model constitutes the 4 (four) statistically most significant macroeconomic variables, that is, determinants of the Chinese economy, which are used for clarifying current trends and can predict the future movements of prices on the global copper market. Statistically significant determinants are the inflation in China, the Chinese export of goods and services, Chinese import of copper, and consumption of refined copper in China. Obtained results of the research show a dominant role of the demand factor and significant influence of the Chinese economy on the movements of the global copper price. At the same time, the results imply that movements of the copper price on the global market can be an indicator of the economic activities slowing down or speeding up, in the countries which are the major traders of this metal.

The existence of a real possibility that the current health crisis could lead to an economic crisis has prompted governments worldwide to make great efforts to sustain their markets. This paper explores the im­pact of COVID-19 and Serbian government anti-Covid activities on the do­mestic stock market using the Autoregressive Distributed Lag (ARDL) Coin­tegration model. In its research, the paper considers the impact of the num­ber of newly infected and the number of deaths from coronavirus daily, as well as measures taken by governments to combat viruses on the represent­ative Belgrade stock exchange index BELEX15. The results showed a signifi­cant long-term negative impact on the number of deaths per day and inter­national travel control on the BELEX15 index. In terms of reducing the neg­ative consequences of the crisis caused by the global pandemic, these re­sults could be a good guideline for effective management of government measures.