The study investigated digital currency and blockchain technology in the 21st century financial ecosystem. The empirical study adopted a descriptive survey design. A questionnaire was used for data collection in a sample size of 121 selected randomly from the staff and students of Abia State Polytechnic, Aba. The data collected from the respondents were analyzed with the frequency distribution table and chi-square (x2 ) statistical technique. The findings revealed the imperativeness of digital currency and blockchain technology in the 21st century financial ecosystem. In other words, digital currency and blockchain technology has significant effect with financial ecosystem. The study, therefore, recommended among others that Central bank of Nigeria, legislators and financial stakeholders should collaborate to establish compliance standards and best practices for digital currency and blockchain integration in financial ecosystem. These standards should ensure that digital currency algorithms and blockchain technology conform with regulatory requirements and ethical principles, while promoting transparency and accountability.
The article examines digital financial assets as an instrument of investment portfolio diversification in the Ukrainian investment context. The study argues that Bitcoin and Ethereum should not be assessed through general statements about financial innovation, but through their measurable contribution to portfolio return, volatility and risk-adjusted performance. The empirical part is based on an annual scenario model for 2020–2025 and compares portfolios with 0%, 1%, 3%, 5% and 10% exposure to BTC and ETH. The benchmark portfolio includes domestic government bonds, the USD/UAH currency component, gold and the S&P 500 as a global equity benchmark, while the local Ukrainian equity segment is interpreted cautiously because of its limited liquidity. The results show that portfolios with 1–5% exposure to BTC and ETH improved risk-adjusted efficiency compared with the baseline portfolio. The P3 scenario provided the most balanced relationship between return growth and risk growth, while P5 generated a higher average annual return with still acceptable volatility. The P10 scenario produced the highest geometric average annual return but almost tripled volatility compared with the baseline portfolio, making it more suitable for an aggressive investor profile. The article concludes that digital financial assets may have practical value only as a limited high-risk addition to a diversified portfolio, not as a stable hedging instrument or a substitute for traditional instruments.
The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.
This study provides an econometric investigation of Bitcoin’s return dynamics using daily data over 5.5 years from January 2020 to September 2025. This research deeply analyses the market behaviour of Bitcoin over other assets like Gold, Silver, Ethereum, Tether, Nifth50, BankNifty. In this analysis we used advanced time series and statistical models such as ARIMA, GARCH(1,1), Rolling GARCH, Half-Life estimation, and EGARCH models to evaluate conditional mean behavior, volatility clustering, persistence, asymmetric shock effects, and regime-dependent risk transmission. With the use of this models, rolling Garch reveals structural instability with persistence decline in later periods. EGARCH results asymmetric shock effects, where negative shocks increases volatility more than positive shocks. Forecasting models suggests that volatility will eventually return to its long term average, but risk is still expected to remain high for some time before normalizing. The analysis reveals strong conditional heteroskedasticity and near-integrated volatility persistence during crisis periods specific around the COVID-19 market collapse (2020), the FTX bankruptcy shock (2022), the April 2024 Bitcoin halving, and the 2025 Bybit exchange hack. Using various data visualizations, the analysis reveals high risky nature of Bitcoin trade with high returns compared to other assets. Deep learning model LSTM reveals the nature that closing price of next day is unpredictable as obvious in case of such high volatile nature of Bitcoin. These findings underline the importance and nature of trading in Bitcoin for individuals who are thinking to invest.
The article substantiates the critical inadequacy of traditional static risk assessment methods (specifically, VaR and standard deviation) for analyzing the effectiveness of integrating Decentralized Finance (DeFi) assets into investment portfolios. It is proven that the returns of DeFi assets are characterized by a non-normal distribution with pronounced «fat tails», which creates a significant risk of underestimating catastrophic losses. The purpose of the study is to develop and theoretically substantiate a methodology for evaluating the effectiveness of DeFi platforms in diversifying investment portfolios. The methodological gap between the requirements of the volatile DeFi market and the limitations of classical financial models is investigated, particularly in the areas of controlling Tail Risk and the dynamic nature of correlational dependence, which critically increases during market shocks (the «correlation-to-one» effect). A four-stage methodology is proposed, which includes the theoretical integration of Conditional Value-at-Risk (CVaR) as a basic coherent measure of extreme risk and a developed algorithm for proactive diversification management based on the DCC-GARCH model. This made it possible to calculate the Optimal Dynamic Hedging Weight, necessary for the daily adjustment of the portfolio structure to prevent the loss of the diversification effect. The comprehensive methodology developed provides a complete cycle of proactive risk management and offers a clear algorithm for making decisions about the structure of an investment portfolio. The scientific and practical significance of the research lies in formulating methodological recommendations and evaluation criteria that ensure a transition from static analysis to proactive risk management in investment activities. The developed methodology provides a toolkit for making informed decisions regarding the optimal share of DeFi assets in a portfolio, combining return maximization with extreme risk minimization. The application of this methodology is beneficial for investors, financial analysts, quantitative strategists, and hedge fund managers working with high-risk and innovative asset classes that require advanced risk control tools.
Abhay Kumar R J, Dr. Bhavya Vikas, Dr. Sharath Ambrosse
The financial sector has been revolutionized by blockchain technology and digital assets, offering novel investment opportunities. Stable coins, in particular, have risen to the fore for their blend of blockchain benefits and moderate price fluctuations. Stable coins differ from other cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), which are known for their volatile price swings, with their stable value, meaning they can be employed in payment, trading, decentralized finance (De Fi), and portfolio management. This study aims to assess USDT and DAI's contribution to investment strategies in the current investment landscape between 2022 and 2026 alongside Bitcoin and Ethereum. The secondary data was analysed via time series analysis, 3 year moving average, rolling volatility, market capitalization, and correlation analysis of data obtained from Coin Market Cap, Coin Gecko, Reserve Bank publications and other financial databases. The results show that USDT and DAI possessed less volatility, more price stability and better capital preservation when compared to traditional cryptocurrencies. The study also finds that inflation, interest rates and US Dollar Index (DXY) affect the performance of stable coins and market demand. While there are regulatory, transparency, and market trust issues to address, stable coins have proven to be a potentially low-risk digital asset. In conclusion, according to the study, USDT and DAI are good investment alternatives for those who are looking for stability in the cryptocurrency market and are either conservative or new investors.
This paper explores how Bitcoin and Ethereum differ from traditional financial assets such as gold, Brent crude oil, the S&P 500 and Apple Inc. in terms of risk, return and integration with the traditional financial market over the period of 2018-2025. The thesis evaluates whether these digital assets can serve as viable components of a diversified investment portfolio. The motivation stems from the recent institutionalization of cryptocurrencies, including the recent approval of spot Bitcoin and Ethereum ETFs and wide public interest. 2858 observations of log returns were used to analyse correlation, multivariate regression, volatility, CAPM regression and Sharpe ratio. The results show that Bitcoin and Ethereum exhibit very low correlations with traditional assets, which supports their ability to act as diversifiers. The regression models revealed that gold and the S&P 500 have small but statistically significant explanatory power for cryptocurrency returns, while Apple Inc. and Brent crude oil do not. Volatility analysis confirms that Bitcoin and especially Ethereum are much more volatile than all traditional assets in the sample. CAPM results show that both digital assets respond positively to market movements, implying slow financial integration. Returns of cryptocurrencies were extremely high, but when the Sharpe ratios were computed, cryptocurrencies showed weak risk-adjusted performance, compared to Apple Inc. and gold. Overall, the findings show that cryptocurrencies are assets with high risk and are driven more by crypto-specific factors, but are increasingly integrating into the broader traditional financial market. They provide diversification benefits but only in small allocations.
Hameer Hussain Shah, Asra Shaikh, Muhammad Mujtaba, Tanveer Hussain Shah
Purpose: The purpose of this study is to examine how the adoption of major cryptocurrencies affects the financial markets of Pakistan. It focuses on three key areas: the gold market, the stock market (KSE-100 index), and the bond market. Design: This research employs a quantitative approach, utilizing regression analysis to investigate the relationship between the adoption of five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple) and their impact on gold prices, stock market performance, and bond price movements in Pakistan. Findings: The results show that cryptocurrencies do affect financial markets, but each coin has a different impact. Bitcoin and Binance Coin increase gold prices, while Cardano and Ripple decrease them. In the stock market, Bitcoin lowers the index, but Ethereum and Ripple increase it. Cardano and Binance Coin have little to no impact on stocks. For bonds, Cardano and Ripple lower prices, while Binance Coin and Ethereum raise them. Bitcoin has a small negative effect on bonds. Originality and Value: This study contribute significantly to reshaping the investment culture in Pakistan, particularly in the context of evolving regulatory frameworks, creating awareness about digital currencies especially cryptocurrencies so that financial investors, portfolio and fund managers can take better informed decisions. Keywords: Cryptocurrency, Blockchain Technology, Financial Inclusion, Bitcoin, Ethereum, Gold Market, Stock Market, and Bond Market JEL Classification codes: G0, G1, G2, O3
Digital transformation (DT) in the energy sector is pivotal in meeting energy transformation challenges. DT is reshaping energy production, distribution, and consumption by integrating advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. While existing research has extensively documented individual technological applications, there remains a significant gap in understanding how these technologies interact synergistically in real-world implementations [11]. Comprehensive analyses comparing digital transformation outcomes across different socioeconomic contexts are limited, particularly regarding the scalability of swarm electrification models. These technologies collectively address the ‘three Ds’ – decentralisation, decarbonisation, and digitalisation – essential for the evolution of modern energy systems. By leveraging these innovations, the sector can significantly enhance efficiency, optimise renewable energy integration, and expand access to underserved regions.One of the most impactful applications of DT is in the realm of decentralised energy systems, exemplified by swarm electrification. This concept, pioneered by Groh et al [3], utilises interconnected solar home systems (SHSs) to form scalable microgrids that evolve from standalone setups to full integration with national grids. These systems empower communities by facilitating energy sharing, reducing operational costs, and creating new income streams. Case studies from Kenya, Madagascar, Yemen, Germany, and Bolivia demonstrate the real-world success of swarm electrification in bridging the energy access gap while advancing sustainability goals.AI plays a pivotal role in digital energy systems by enabling predictive maintenance, optimising energy flows, and improving system reliability. Algorithms analyse vast datasets in real time to forecast energy demand, detect anomalies, and automate grid management. IoT further complements AI by providing the physical infrastructure to gather and transmit data, enabling real-time monitoring and control of energy assets. Together, AI and IoT support the development of smart grids and energy communities, fostering greater flexibility and resilience in energy networks.Blockchain technology is emerging as a transformative tool for energy trading and distribution. By enabling peer-to-peer (P2P) energy markets, blockchain enhances transparency and reduces transaction costs. This decentralisation of energy trading allows consumers to become prosumers, actively participating in energy production and exchange. Projects such as Esmat et al. decentralised platforms exemplify how blockchain empowers individuals and communities to take ownership of their energy futures while ensuring security and scalability.Despite these advancements, the implementation of digital technologies in energy systems is facing significant challenges. High initial costs, the complexity of integration, and cybersecurity risks pose barriers to widespread deployment. Furthermore, the digital divide in underserved regions limits equitable access to these transformative solutions. Environmental concerns related to the energy consumption of digital infrastructures, such as data centres and blockchain networks, also require attention. Addressing these issues necessitates a multi-stakeholder approach involving policymakers, industry leaders, and researchers to create enabling environments for innovation.This review provides a comprehensive analysis of the role of DT in advancing energy systems, focusing on AI, IoT, blockchain, and swarm electrification. It synthesises insights from over 100 scholarly sources, including real-world case studies, and evaluates the social, economic and technological impact of digitalisation on energy systems. The study adopts a mixed-method approach, integrating literature analysis, quantitative modelling, and case study evaluations to provide actionable insights for policymakers and industry practitioners.The findings of this review highlight the transformative potential of DT in addressing energy challenges, particularly in achieving the United Nations Sustainable Development Goal 7 [2]: universal access to affordable, reliable, and modern energy. By adopting digital innovations, energy providers can enhance operational efficiency, integrate renewable energy sources, and support community-based energy initiatives. The concept of swarm electrification exemplifies how decentralised approaches can complement centralised grids, ensuring scalability and adaptability to local needs.Policy recommendations emphasise the need for financial incentives, capacity-building programmes, and regulatory frameworks to facilitate digital adoption. Investment in human capital is particularly critical, as skilled personnel are required to implement and manage complex digital systems. International cooperation and knowledge sharing are essential to ensure digital transformation efforts align with global sustainability goals.In conclusion, DT represents a paradigm shift in energy systems, offering solutions to some of the sector’s most pressing challenges. Realising its full potential requires overcoming technical, financial, and institutional barriers. This review underscores the importance of a collaborative, multidisciplinary approach to harnessing the power of digital technologies for sustainable energy transitions.
Background: Bitcoin mining, an energy-intensive process, requires significant amounts of electricity, which results in a particularly high carbon footprint from mining operations. In the Republic of Kazakhstan, where a substantial portion of electricity is generated from coal-fired power plants, the carbon footprint of mining operations is particularly high. This article examines the scale of energy consumption by mining farms, assesses their share in the country’s total electricity consumption, and analyzes the carbon footprint associated with bitcoin mining. A comparative analysis with other sectors of the economy, including transportation and industry is provided, along with possible measures to reduce the environmental impact of mining operations. Materials and methods: To assess the impact of bitcoin mining on the carbon footprint in Kazakhstan, electricity consumption from 2016 to 2023, provided by the Bureau of National Statistics of the Republic of Kazakhstan, was used. Data on electricity production from various types of power plants was also analyzed. The Life Cycle Assessment (LCA) methodology was used to analyze the environmental performance of energy systems. CO2 emissions were estimated based on emission factors for various energy sources. Results: The total electricity consumption in Kazakhstan increased from 74,502 GWh in 2016 to 115,067.6 GWh in 2023. The industrial sector’s electricity consumption remained relatively stable over this period. The consumption by mining farms amounted to 10,346 GWh in 2021. A comparative analysis of CO2 emissions showed that bitcoin mining has a higher carbon footprint compared to electricity generation from renewable sources, as well as oil refining and car manufacturing. Conclusions: Bitcoin mining has a significant negative impact on the environment of the Republic of Kazakhstan due to high electricity consumption and resulting carbon dioxide emissions. Measures are needed to transition to sustainable energy sources and improve energy efficiency to reduce the environmental footprint of cryptocurrency mining activities.
This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process. We cannot, therefore, vouch for the reliability or integrity of this article. Please note that this notice is intended solely to alert readers that the peer-review process of this article has been compromised. Wiley and Hindawi regret that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.
This work investigates the factors determining the Kazakh energy crisis which occurred in the second half of 2021. From the correlation observed among some data gathered to the purpose of the analysis, the relevant role played in this by cryptocurrency mining factories is identified. Beginning from June 2021, a massive number of them were relocated to Kazakhstan from the Popular Republic of China (PRC) because of normative restrictions introduced by the latter. The work also develops a reflection aimed at understanding the economic and environmental impact which has been produced by this relocation. The descriptive analysis will proceed as follows: the first section of the article will focus on the regulation of cryptocurrencies; the second section will focus on final electricity consumption and sup-porting empirical evidence and is closely related to the third and last section; the latter will focus on primary macro-economic indicators in relation to the increase in CO2 emissions in the Kazakh republic. To this end, it is useful to demonstrate a correlation between the energy crisis, the transfer of cryptocurrency mining to Kazakhstan, and to fuel the discussion regarding the need for a supranational institution with the aim of codifying a common international legislation, thus reinforcing the efforts made so far in this direction. Present and future implications and scenarios de-rived by the analysis are also introduced.
Access to affordable, reliable and clean energy is an important sustainability goal of the United Nations. In areas where the public electricity grid is unreliable or unavailable, photovoltaic systems can be a solution. However, they are cost-intensive, mainly because of the energy storage systems. Mini-grids can be an answer for reducing upfront investment and overall system lifetime costs while increasing electricity availability. The mini-grid technology is mature, nevertheless, there are downsides when it comes to integrating existing solar systems of different manufacturers. The system topology is usually predefined and a central instance controls the mini-grid. Thus, the integration of existing power systems is difficult due to the communication constraints of these systems with the mini-grid controller. Including existing power systems into a decentralized mini-grid, can highly increase cost-efficiency. In a decentralized approach payments for the consumed energy between mini-grid actors are required. Accounting is, however, a complex administrative procedure, if the respective power systems are owned by different individuals and organizations. A transparent blockchain-based temper-proof approach can be a solution to automate metering and billing, allowing automatic payments between independent subsystem owners using smart contracts. In order to further optimize the smart mini-grid, an artificial intelligence learning algorithm for a dynamic electricity price needs to be developed. This smart and decentralized approach for building Mini-Grids is a novelty bringing solar systems one step closer to self-sufficiency. This paper describes how a smart mini-grid solution can be implemented using the Don Bosco Solar & Renewable Energy Center campus mini-grid in Tema, Ghana as a case study.