http://ej.hse.ru en-us Copyright 2025 Wed, 15 Oct 2025 11:31:22 +0300 https://ej.hse.ru/en/2025-29-3/1093194040.html In recent years cryptocurrencies have gained popularity: institutional and retail investors as well as different trading companies started positively looking at crypto as sets. As a result, the convergence level between cryptocurrencies and the traditional markets has changed a lot. Stocks deserve close attention because they are a key asset for many investors.This article explores the evolving relationship between crypto assets and the US stock market by focusing on the four largest cryptocurrencies: Bitcoin (BTCUSDT), Ethereum (ETHUSDT), Ripple (XRPUSDT), Binance Coin (BNBUSDT), and the US stock index (S&P500). Utilizing correlation analyses of returns and volatilities alongside Granger causality tests, the study reveals how these markets affect each other. To implement the analysis, 1-minute and 5-minute data for 4.5 years, from 2019 to mid-2024, was used. Employing intraday granular data distinguishes this study from earlier ones that used daily data, and also identifies conclusions that may be more interesting to market participants such as scalper traders. The results indicate that cryptocurrencies become more integrated with traditional financial markets during periods of global economic instability, such as COVID-19 in 2020 and geopolitical tensions in 2022. Moreover, according to Granger causality tests, connections become bidirectional in stressed years, indicating the mutual influence between stocks and cryptocurrencies. On the other hand, in quieter times, the level of interconnection is significantly reduced in terms of correlations. Granger causality results reveal mostly unidirectional relationships (the impact of the US stock market on all 4 cryptocurrencies) during calm periods. https://ej.hse.ru/en/2025-29-3/1093209430.html The article analyses the economic content of the process of tokenisation of money and assets, identifies the main types and forms of tokenised money, outlines the key characteristics of central bank digital currencies, secured Stablecoins and tokenised deposits. The study alsoconsiders the features of new funding mechanisms resulting in the issuance of investment, utilitarian and hybrid tokens, identifies the main types of tokenised financial and real assets, identifies the benefits and risks of tokenisation, and outlines global trends in the field of tokenisation and defines the features of the current development of the CFA market in Russia. The study concludes that the transition from the electronic form of money and assets to the to kenised form marks not only the transformation of the form of value, but also its unification with the network of financial messaging, which makes it possible to ensure the transition from the stage of digital finance to the stage of institutional decentralised finance. The main types of tokenised money currently available are central bank digital money and tokenised money of CBs and FIs. While the former are issued as central bank digital currencies for retail payments and/or wholesale settlements, the latter are issued as tokenised deposits and/or secured Stablecoins. Tokenisation of money does not change its economic nature, but it allows digital forms of money to be endowed with new properties and a wider range of functions. The widespread use of tokenised money can speed up transactions, increase their transparency and reduce transaction costs. The emergence of native investment, utilitarian and hybrid tokens as a result of innovative ICO, IEO and IDO financing mechanisms in the crypto market has contributed to the expansion of forms and simplification of procedures for access to financing for startups and has become a precursor to the wider tokenisation of various types of financial and real assets. Currently, the main types of tokenised assets are: 1) tokenised tradable liabilities of private money issuers; 2) tokenised commodities; 3) tokenised government securities; 4) tokenised stocks, corporate bonds and units in private money market funds; 5) tokenised real estate; and 6) tokenised private credit. The main advantages of tokenisation of financial and real assets include increased capital efficiency; democratisation of market participant access; savings in transaction costs; increased regulatory compliance, auditability and transparency; cheaper and more flexible infrastructure. However, high implementation costs, regulatory uncertainty, lack of a harmonised financial market infrastructure, etc. are significantly constraining the development of asset tokenisation in most countries around the world. Tokenisation of financial and real assets in Russia, carried out in the CFA market, differs from tokenisation in the global market by the presence of institutional, platform and marketing peculiarities due to regulatory restrictions. These restrictions, on the one hand, contribute to reducing the risks of market participants, and on the other hand, hinder the development of this market by limiting its liquidity and reducing the possibility of foreign issuers and investors to participate in it. https://ej.hse.ru/en/2025-29-3/1093222823.html The main results of the work are new Bayes–Nash equilibria related to the theory of oligopoly. The article is divided into two parts. The first part considers the classical problem of price competition with product differentiation. (That is, each firm produces one product, but the products differ. This is the next most complex class of models after the models where it is assumed that all firms produce a homogeneous product. It is often believed that the goods produced by firms are substitutes.) Each firm knows its marginal costs, but the marginal costs of competing firms are not known, that is, a game with incomplete information is considered. A special feature of this work is that the probabilistic model (for marginal costs) has a very general form. Concerning the joint distribution of random variables, it is only assumed that each random variable has a finite expectation. The second part of the article considers a more complex form of competition according to Bertrand. This part of the article refers to the scientific direction that is sometimes called "price and quality competition". Firms choose not only prices, but also quality levels of the products they produce. At a higher quality level, demand for products increases (at a fixed price), but the firm's costs also increase. Here, a two-stage game is considered. First, firms choose quality levels, and then prices. In the second part of the article, conditions are imposed that random variables have not only finite expectations, but also finite variances. It is assumed that the demand function is linear and that the firms' cost functions are also linear (relative to output volumes). Explicit expressions are given for equilibrium prices and outputs. https://ej.hse.ru/en/2025-29-3/1093239577.html The paper assesses the impact of China’s carbon regulation on the Russian economy. It examines the scenario which is not highly probable but has been discussed in Russia–namely, the potential introduction of carbon border regulation on imports of carbon-intensive products by China. The study employs a scenario-based approach using computable general equilibrium models GTAP-Power and GTAP-E, developed by the Global Trade Analysis Project (GTAP). The GTAP 10 data are used to calibrate the model, and preliminarily adjusted to Western sanctions imposed on Russian trade flows in 2022–2023. The results indicate that the introduction of carbon border regulation by China has virtually no impact on the output of Russia’s energy- intensive industries. The non-ferrous metal sector increases exports due to the relatively low carbon intensity of Russian production compared to other countries, whereas the chemical industry appears to be sensitive to the hypothetical implementation of China’s carbon border regulation. The introduction of a domestic carbon price in Russia mitigates the risks for exports to China; how ever, it leads to a relatively small decline in real GDP–p to –0.06%–at a carbon price of $20 per ton of CO₂ equivalent (in 2014 prices). The most sensitive sectors to national carbon regulation include coal production, natural gas extraction, and iron and steel industry. https://ej.hse.ru/en/2025-29-3/1093241211.html The Russian academic sector is characterised by the absence of a strong academic market, high level of university inbreeding and differentiation. Universities can be divided into teaching- and research-oriented. Different aims of universities make them create different incentive schemes for faculty. We focus on estimating the determinants of “success” of Russian faculty in different types of universities. We measure success as the percent deviation of an individual’s wage from the average university wage to account for the competition inside, rather than outside universities. According to our results, both types of universities require publishing activity from faculty, but research-oriented universities pay great attention to the top journals. Time spent on teaching is a significant success determinant at any type of university – research-oriented and teaching-oriented ones. However, faculty need to spread their time between teaching and research carefully, otherwise they can be penalised relative to their colleagues. Holding a formal administrative post does not guarantee financial success, while performing informal administrative duties leads to financial success in teaching-oriented universities. The results may suggest that some Russian universities should revise their missions to focus more on teaching, given that research is already concentrated in leading universities. This would prevent the Russian higher education sector from becoming overly research- oriented and ensure attention to the volume and quality of education. https://ej.hse.ru/en/2025-29-3/1093254473.html Revenue from the sale of TV rights is one of the key items for European football clubs, often accounting for a third to a half of their revenues. In Russia, media rights bring clubs on average only 10% of all revenue. At the same time, the Russian Premier League uses a rather primitive model of revenue distribution, which cannot fully stimulate the development of football and considers the interests of small clubs to a lesser extent. Russian football is also characterized by a significant gap in revenue: the revenue from the sale of TV rights of the clubs ranked first and last in the RPL differs by more than 2.5 times. This paper analyses the experience of TV revenue distribution in European championships, and based on the criteria that have already been successfully applied in the leading leagues, a new model that meets the modern requirements and challenges of Russian football is proposed. Among the new distribution criteria, the authors introduce indicators characterizing communication with fans and the development of Russian young players. Application of the model showed a reduction in the financial gap in media revenue between the clubs ranked first and last to 1.86 times, which will enhance competition within the championship and, consequently, ensure the growth of viewer interest and total revenues.