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In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural gas, fuel oil, the authors compared the effectiveness of forecasting models of generalized autoregressive heteroscedasticity (Generalized Autoregressive Conditional Heteroscedastic model, GARCH) with regression of support vectors for futures contracts. GARCH models are a standard tool used in the literature on volatility, and the vector machine nonlinear regression model is one of the machine learning methods that has been gaining huge popularity in recent years. The authors have shown that the accuracy of volatility forecasts for energy and aluminum prices significantly depends on the volatility proxy used. The model with correctly defined parameters can lead to fewer prediction errors than GARCH models when the square of the daily yield is used as an indicator of volatility in the evaluation. In addition, it is difficult to choose the best model among GARCH models, but forecasts based on asymmetric GARCH models are often the most accurate. The work is based on a model with a representative investor who solves the problem of optimizing utility in a two-period model. The key assumption of the model is the homogeneity of energy and aluminum investor preferences, that is, preferences do not change over time. There are also works with an attempt to solve this problem in a continuous state space. A completely new theory has been put forward that allows predicting the movement of the underlying asset without using historical data, so this topic is very relevant.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.

In this study, it is examined how to improve the effectiveness of bitcoin market. In this framework, firstly, a wide literature review is made, and different factors that may be effective in this regard are identified. Afterward, an analysis is performed with Hurst method to determine which of these factors are more important. The study of cryptocurrency volatility is important in terms of financial instruments for hedging traditional assets, as well as in terms of pricing. The above results would be particularly useful in terms of portfolio management and risk management. This study discusses various financial issues related to cryptocurrencies, as well as some problems associated with the problems of their forecasting. To solve the problems of forecasting, the authors have not only studied, but also improved the forecasting method based on the machine learning algorithm in the MATLAB system using the tree model. Most of the analysis in the document relies heavily on studies by authors studying this issue. They can help make more informed decisions regarding financial investments and avoid the risks of using cryptocurrencies. The results may also assist regulators in creating a favorable legal environment for the functioning of the cryptocurrency market.

A retrospective study of the interdependence of financial development and energy investment can show us that the opinions of scientists have changed quite dynamically in previous centuries regarding the role of the financial sector in the economy. This is primarily due to technological innovations and economic transformations. At the same time, it is worth mentioning the main approaches and directions prevailing in economics over the past time. The founder of the theory that financial markets have a positive impact on the development of the economy is the English economist Walter Bagehot. In his work, published in 1873, the author analyzes the economic processes of that time and, as a result, gets a general description of the ongoing interactions between the financial and real sectors. His most important conclusion for us is that loan capital, according to the economist, contributes to the expansion of production.

The purpose of this study is to analyze structural changes that took place in the cotton industry and develop a statistical model that reflects the current drivers of U.S. upland cotton prices. This study concludes that a structural break in the U.S. cotton industry occurred in 1999, and that world cotton supply has become an important determinant of U.S. cotton prices. The model developed here forecasts changes in U.S. cotton price based on changes in U.S. cotton supply, changes in U.S. stocks-to-use ratio (S/U), changes in China's net imports as a share of world consumption, the proportion of U.S. cotton engaged in the loan program, and changes in world supply of cotton.

The main purpose of this chapter is to show the correlation of the current consolidated electricity capacity of the Eurasian Economic Union (EAEU) with the forecast indicators of electricity consumption to optimize the future energy system. It highlights the main directions of the international cooperation development in the field of hydropower and analyzes the total consumption of hydropower in the EAEU. The study is based on official data provided by international hydropower and renewable energy regulators as well as national professional regulators and statistical offices. Authors predicted total hydropower consumption of the EAEU countries for the coming years using machine learning algorithms and interpreted obtained results by an econometric toolkit. It is shown that the current hydropower capacity level will not cover future consumption, in particular, due to the increasingly growing demand for cheap electricity because of massive digitalization as one of the global pandemic impacts. As a result, the necessity was identified to gradually increasing the available hydropower capacity to balance the situation. In conclusion, it's been proposed potentially possible solutions to optimize the future energy system of the single energy market of the EAEU to achieve the required level of electricity generation from power plants operating on renewable energy sources and, in particular, water resources, taking into account the consequences of Covid-19 in the energy industry.

In modern conditions energy investment in Russia the world and cash flow status sectors is sufficient tense: financial institutions are forced to act in the following conditions: deficiencies in money supply and underestimated financial assets and liabilities of active site balance of payments. However, fast ones actions regulators contribute to mitigation measures the crisis. This very clearly shows the importance of monetary policy in Russia in regards to modern economic relations. Modern reality of development of energy investment and a new period of functioning of the economy were determined by necessity for revision of theoretical data basics and practical analysis of monetary policies, which determine the basic direction of this study. Monetary policy does not apply to financial institutions. It is fully autonomous, as end goals of monetary and credit control system regulations coincide with basic principle objectives of the macroeconomic policies of countries.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The different methods were used to measure the impact of financial development on economic growth. This is largely due to a certain set of factors that have the influence of their relationship. First of all, based on the research results, several groups of factors are identified that determine the nonlinear interaction of the financial sector and economic growth. This is characterized by the fact that at different values of these indicators, financial development has an ambiguous impact on economic growth. There are several such groups in total. The first ones are usually institutional factors, indicators that characterize the legal system and the level of development of social institutions. In the absence of a sufficient level of legal framework, financial markets will be unstable, which will undermine the demand for financial sector services. Countries with a high level of development of legal institutions have a guarantee for economic entities in the stable development of economic relations.