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Aluminum is an exchange commodity. But physical trading of this metal in most cases does not occur on the stock exchange, since more than 90% of aluminum sales with physical delivery occur under direct contracts between producers and buyers of the metal (over-the-counter market). Aluminum as an exchange commodity has standardized consumer properties, namely: the goods are interchangeable, easily transported and stored, and can be divided into batches. That is why upstream products are traded on commodity exchanges, not semifinished products or finished products. When commodity exchanges were first created, they served as a place for concluding physical contracts for the supply of such exchange-traded goods, but with the increase in trading volumes and the development of financial instruments, the role of exchanges has changed. Today, futures contracts for raw materials are traded on them – financial instruments that almost never entail a real physical supply (at the same time, this possibility is not excluded). As a result of the bidding, a price is set that serves as a guideline for producers and consumers around the world.

The military operation of the Russian Federation on the territory of Ukraine exerted additional pressure on prices on the aluminum market since aluminum supplied by Russia accounts for about 10% of the total volume of US imports. It is known that Russia has become the largest aluminum producer after China, and now there is also an increase in aluminum production. Since electricity prices remain relatively low in Russia, especially in energy-surplus Siberia, the increase in output along with the increase in metal prices is a positive factor, since 70% of the primary aluminum produced is exported and only less than a third is consumed domestically. At the same time, high aluminum prices may constrain the expansion of domestic consumption of the metal and may force manufacturers to look for a cheaper alternative. In general, the increase in aluminum prices coincides with the general “supercycle” of raw materials in the last year and a half, and there is a chance to stabilize aluminum prices at current high levels, which will be facilitated, among other things, by new metallurgical projects in Russia. At the same time, it is worth noting that limited metal supplies will haunt the industry for most of 2022, and some experts predict that it may take up to five years to solve the problems.

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.

Bioenergy remains the largest branch of renewable energy, and microalgae are a promising object of research among other types of biomasses whose scale for energy purposes is increasing. On the other hand, the growth of global energy production and urbanization, which results in high rates of municipal waste and wastewater generation, requires the development of integrated technologies that allow waste to be disposed of as fully as possible. Sustainable investments in the production of energy by various technologies are one of the methods to solve this complex problem. In this chapter, we study the methods of microalgae utilization of nutrients from wastewater and by-product liquid waste of sustainable investments from microalgae by hydrothermal liquefaction (HTL) technology. Wastewater has a complex composition, and the treatment of nitrogen and phosphorus and other biogenic elements, as well as heavy metals, using biological objects is optimal and cost-effective. Also the water phase after HTL is a by-product that has limited energy value. Biofuel investments have higher growth rates and at the same time do not compete with the investments in fossil fuels. Biofuel investments' cost of seaweed fuel can be reduced through high-value-added related products, such as food and feed additives, and pharmaceutical and cosmetic products.

This chapter examines the ways social media, analytics, and disruptive technologies are combined and leveraged by political campaigns to increase the probability of victory through micro-targeting, voter engagement, and public relations. More specifically, the importance of community detection, social influence, natural language processing and text analytics, machine learning, and predictive analytics is assessed and reviewed in relation to political campaigns. In this context, data processing is examined through the lens of the General Data Protection Regulation (GDPR) effective as of May 25, 2018. It is concluded that while data processing during political campaigns does not violate the GDPR, electoral campaigns engage in surveillance, thereby violating Articles 12 and 19, in respect to private life, and freedom of expression accordingly, as stated in the 1948 Universal Declaration of Human Rights.