Power exchanges in Europe have been operating since the 1990s, while in the region of Southeast Europe (SEE) they are only a few years old. Volatility is considered to be one of the indicators that define the level of market development. In this paper, the authors examine 15 power markets in Europe with the novelty of special attention dedicated to the SEE markets, in terms of their volatility. The aim of this paper is to investigate volatility on SEE markets, and their correlation with developed European markets. Power price volatility is measured by price velocity concepts, the daily velocity based on daily/monthly/quarterly/yearly and overall average power prices. The outcomes reveal that power price volatility is higher in new SEE markets than in more mature European markets. The least volatile market is the Greek pool, followed by Switzerland and Austria. The Bulgarian market is the most volatile, followed by that of Romania, and these markets are nearly twice as volatile as the ones previously mentioned. A correlation matrix is carried out and confirms positive correlation between all markets in terms of their average prices. However, a correlation matrix of measured volatilities depicts a negative correlation, in some cases, between SEE and Central European (CE) markets.

This paper analyzes the impact of corruption level on East European financial markets development. Financial market liquidity for 15 national markets is presented and market volume per capita is used as an indicator for market maturity. Market volume per capita values are compared to corruption perception index values, using classical logic method. Findings of the research are quite interesting and unexpected, as they show modest impact of corruption on financial market development. Results suggest that further research of corruption should be done, in order to develop better, quantitative corruption indicator.

Volatile energy prices in the past period, force countries and their market participants to find alternative and cheaper sources of electricity production, while respecting defined environmental principles. Serbia is one of the rare European countries that uses its reserves of dissolved gas in the heat and electricity production. Combined heat and power plants, although in an almost negligible amount, contribute to the diversification of Serbia's production mix. With the presence of organized markets and local power exchanges, sale of electricity from combined heat and power plants is guaranteed for all producers who do not meet the conditions for acquiring the right to feed-in tariffs. The aim of this paper is to analyze the most profitable sale on the power exchanges of the domestic market and the region, specifically the markets of Serbia, Croatia, Hungary, Romania, and Bulgaria. The calculation methodology itself implies the use of average monthly price value for the observed markets, in the period of the past three years and nine months. In addition to the aforementioned, the costs of cross-border capacities were considered. In order to have a clearer overview of the results and the possibility of applying the methodology, the amount for calculation is 1 MWh. Costs and revenues related to negative and positive balance energy deviations are excluded from the analysis. Results showed that sale of electricity is most profitable on Serbian power exchange.

Introduction/purpose: The paper introduces a reduced latency stack decoding algorithm of polar codes, inspired by the bidirectional stack decoding of convolutional codes and based on the folding technique. Methods: The stack decoding algorithm (also known as stack search) that is useful for decoding tree codes, the list decoding technique introduced by Peter Elias and the folding technique for polar codes which is used to reduce the latency of the decoding algorithm. The simulation was done using the Monte Carlo procedure. Results: A new polar code decoding algorithm, suitable for parallel implementation, is developed and the simulation results are presented. Conclusions: Polar codes are a class of capacity achieving codes that have been adopted as the main coding scheme for control channels in 5G New Radio. The main decoding algorithm for polar codes is the successive cancellation decoder. This algorithm performs well at large blocklengths with a low complexity, but has very low reliability at short and medium blocklengths. Several decoding algorithms have been proposed in order to improve the error correcting performance of polar codes. The successive cancellation list decoder, in conjunction with a cyclic redundancy check, provides very good error-correction performance, but at the cost of a high implementation complexity. The successive cancellation stack decoder provides similar error-correction performance at a lower complexity. Future machine-type and ultra reliable low latency communication applications require high-speed low latency decoding algorithms with good error correcting performance. In this paper, we propose a novel decoding algorithm, inspired by the bidirectional stack decoding of classical convolutional codes, with reduced latency that achieves similar performance as the classical successive cancellation list and successive cancellation stack decoding algorithms. The results are presented analytically and verified by simulation.

This paper aims to test the hypothesis that higher ESG scores lead to a lower cost of capital for publicly traded companies in Eastern Europe. In recent years, ESG factors have become increasingly important in the investment industry, influencing the cost of capital for publicly traded companies from banking industry. As companies seeking capital are increasingly scrutinized based on ESG factors, most publicly traded companies publish ESG disclosures and strive to be ESG compliant. Although there is no definitive governing body for ESG, capital providers benchmark companies based on ESG scores—either internally created or from external providers—and integrate these scores into their investment decisions. With cost of capital on the rise during previous period, the authors want to test the hypothesis that ESG score have positive influence on the cost of capital for public companies in banking industry, with lower cost of capital compared to their peers. Using the Weighted Average Cost of Capital (WACC), this paper breaks down the cost of capital to test the effect of ESG scores on both the cost of debt and the cost of equity for publicly traded banking corporations from Eastern Europe.

The growth in electricity demand driven by artificial intelligence and digital infrastructure is directing attention toward nuclear energy as a stable source. The sector has been transformed by the emergence of SMRs and the growing number of publicly listed developers. The aim of this paper is to investigate whether the stock returns of publicly listed SMR companies are more closely associated with the AI-related equity benchmark or with the energy commodity benchmark. Due to the absence of a unified SMRs index, a market-capitalization-weighted synthetic NUC index was constructed from Oklo Inc., NuScale Power Corporation, and NANO Nuclear Energy Inc. for the period from 1 July 2024 to 1 July 2026. The Global X Artificial Intelligence & Technology ETF (AIQ) and The S&P GSCI Energy Spot Index represented the AI-related equity and energy commodity benchmarks, respectively. The relationships were examined using correlation and regression analyses with Newey–West HAC standard errors, based on 501 daily logarithmic returns. NUC increased by 166.80%, versus 77.79% for AIQ and −7.68% for the energy benchmark. Correlations were 0.510 (p < 0.001) with AIQ and −0.059 (p = 0.186) with the energy benchmark; standardized coefficients were 0.509 and −0.046, respectively, differing significantly (one-sided p < 0.001).