Search Results (99)

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases, the lockdown stringency index, and exchange rate volatility. To achieve the above objectives, we have employed advanced econometric techniques, such as wavelet coherence and a hybrid non-parametric quantile causality framework, on the dataset spanning from 30 December 2020 to 24 January 2022. Robustness is assessed using Troster–Granger causality in quantiles and Breitung–Candelon Spectral Causality tests. The wavelet coherence analysis indicates that the initial outbreak of COVID-19 increased the exchange rate volatility, while the enforcement of stringent lockdowns in the later phases helped reduce this volatility. Similarly, the hybrid quantile causality results indicate that both COVID-19 cases and lockdown measures possess predictive power over exchange rate fluctuations. The robustness checks confirm these findings and establish a causal relationship between the pandemic, policy responses, and currency market behaviour. This study helps clarify the complex, nonlinear dynamics between pandemic-related variables and exchange rate volatility in emerging markets. Based on the aforementioned result, it is recommended that policymakers implement targeted lockdown strategies coupled with timely monetary interventions (such as foreign exchange reserve management or interest rate adjustments) to mitigate volatility and maintain currency stability during future pandemic-induced shocks. Full article

Banking has evolved from ancient times of using grain banks and temple lending to modern banking practices. The transformation of the banking sector has ensured that banks play the crucial role of facilitating faster and efficient service delivery. This paper traced the evolution of banking and examined associated disruptions, opportunities, and challenges. With the specific objective of influencing policy-oriented discussions on the future of banking, this study adopted a literature review methodology of integrating various sources, such as scholarly journals, policy reports, and institutional publications. Public interest theory and disruptive innovation theory underpinned this study. Findings revealed that banking has evolved from Banking 1.0 to Banking 5.0 due to disruptive factors which have been pivotal to the significant structural sector changes: Banking 1.0 (pre-1960s); Banking 2.0 (1960s to 1980s); Banking 3.0 (1980s–2000s); Banking 4.0 (2000s–2020s); and Banking 5.0 (2020s to the future). Despite the existence of opportunities in the transformation, challenges include regulations, skills shortages, legacy systems, and cybersecurity that must be addressed. This calls for a coordinated response from stakeholders, with banking’s future requiring collaborations as cashless economies, digital economies, and digital currencies take centre stage. Full article

This paper measures the post-treatment effects of Central Bank Digital Currencies (CBDCs) on mobile money and outstanding loans from commercial banks as a percentage of the GDP in Nigeria and the Bahamas, respectively, from the perspective of financial inclusion. The literature on the topic has primarily focused on the technological specifications of CBDCs and their potential future implementation. This article addresses a gap in the empirical literature by examining the effects of CBDCs. To this end, a Synthetic Control Method (SCM) is applied to the Bahamas (SandDollar) and Nigeria (eNaira) to construct a counterfactual scenario and assess the impact of CBDCs on mobile money and commercial bank loans. Nigeria’s mobile money transactions as a percentage of the GDP increased significantly compared to the synthetic control group, suggesting a notable positive effect of the eNaira. Conversely, in the Bahamas, actual performance fell below the synthetic control, implying that SandDollar may have contributed to a decline in outstanding loans. These results suggest that CBDCs could pose a “deposit substitution risk” for commercial banks. However, they may also enhance the performance of other Fintech tools, as observed in the case of mobile money. As CBDC implementations worldwide remain in their early stages, their long-term effects require further analysis. Full article

Oil, as a key commodity in international markets, bears an importance for both producers and consumers. For oil-exporting countries, periodic fluctuations have a considerable impact on the economic status and the way monetary and fiscal policies should be conducted in the future. While most of academic efforts tried to link low-frequency real exchange rate with macroeconomic fundamentals for medium-/long-term inference, they omitted to gauge the volatile and complex high-frequency linkage between oil prices and exchange rate fluctuations. The inherent non-linear characteristics of such time series preclude the use of traditional tools or aggregated schemes based on lower frequencies for inference purposes. This work investigates the scale-based volatile linkage between daily international oil fluctuations and nominal exchange rate variations of an oil-exporting country, namely Algeria, by adopting a fractal regression approach to uncover the power-law, time-varying transmission and track its incidence in the short and long runs. Results show the absence of any short-term transmission mechanism from oil prices to the exchange rate, as the two variables remain decoupled but exhibit an increasing negative correlation when long scales are considered. Furthermore, the multiscale regression analysis confirms the existence of a scale-free, two-state Markov switching regime process generating short- and long-term impacts with sizeable amplitudes. The findings confirm the usefulness of monetary policy interventions to stabilize the local currency, as the source of Dollar–Dinar multifractality was found to be the probability distribution of observations rather than long-range correlations specific to oil prices. Full article

This study analyses the economic impact of tourism in Iceland, focusing on its contributions to GDP, employment, and foreign currency earnings. This study employs descriptive and comparative secondary data analysis based on available statistics and an extensive literature review to assess the sector’s development, resilience, and sustainability within global and national contexts. The findings confirm that tourism is a key pillar of Iceland’s economy, surpassing traditional export industries in value and generating significant employment opportunities. However, the sector’s volatility exposed during the COVID-19 pandemic and its dependence on international markets reveal structural vulnerabilities that threaten a sustainable future. Beyond economic considerations, this study critically engages with the growing pressures of over-tourism, seasonality, and environmental degradation, particularly in ecologically sensitive areas. Recent scholarship and policy shifts emphasise the need for sustainability indicators, equitable taxation mechanisms, and participatory governance to guide Iceland’s tourism development. This research highlights that balancing economic growth with environmental limits and community well-being is essential for building a more resilient and future-proof tourism model. These insights help inform policymakers, stakeholders, and researchers in aligning tourism strategies with sustainability and diversification goals. Full article

Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer detection, environmental monitoring, mining, mineralogy, and food processing, specifically highlighting significant achievements from the past five years, providing a timely update across several fields. It also presents a cross-disciplinary classification framework to systematically categorize applications in medical, agriculture, environment, and industry. In counterfeit detection, HSI identified fake currency with high accuracy in the 400–500 nm range and achieved a 99.03% F1-score for counterfeit alcohol detection. Remote sensing applications include hyperspectral satellites, which improve forest classification accuracy by 50%, and soil organic matter, with the prediction reaching R² = 0.6. In agriculture, the HSI-TransUNet model achieved 86.05% accuracy for crop classification, and disease detection reached 98.09% accuracy. Medical imaging benefits from HSI’s non-invasive diagnostics, distinguishing skin cancer with 87% sensitivity and 88% specificity. In cancer detection, colorectal cancer identification reached 86% sensitivity and 95% specificity. Environmental applications include PM2.5 pollution detection with 85.93% accuracy and marine plastic waste detection with 70–80% accuracy. In food processing, egg freshness prediction achieved R² = 91%, and pine nut classification reached 100% accuracy. Despite its advantages, HSI faces challenges like high costs and complex data processing. Advances in artificial intelligence and miniaturization are expected to improve accessibility and real-time applications. Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. The development of lightweight, portable HSI devices will enable more on-site applications in agriculture, healthcare, and environmental monitoring. Moreover, real-time processing methods will enhance efficiency for field deployment. These improvements seek to enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments. Full article

This paper introduces a token-based digital currency (TBDC) model for standardizing service delivery in an aviation technical support as a service (ATSaaS) platform. The model addresses the challenges of service standardization and valuation by integrating cost, time, and quality parameters into a unified framework. Unlike traditional cryptocurrencies, this specialized digital currency incorporates intrinsic service valuation mechanisms that dynamically reflect the worth of aviation technical support services. The research presents a mathematical formulation for token value calculation, including a Service Passport framework for comprehensive documentation and a systematic approach for service integration. The model is validated through a numerical case study focusing on maintenance, repair, and overhaul services, demonstrating its effectiveness in generating fair token values across diverse service types. The study introduces optimization techniques using machine learning to enhance token calculations, successfully standardizing heterogeneous services while maintaining flexibility and transparency. Implementation challenges and future developments are identified. The TBDC model provides a foundation for transforming aviation technical support services, particularly benefiting small airlines through improved efficiency, standardization, and accessibility. Full article

This study aims to identify the primary factors influencing the demand for money in Indonesia and to provide forecasts through 2033. The research employs two methodologies: time series econometrics and machine learning, utilizing data spanning from the first quarter of 2010 (2010Q1) to the fourth quarter of 2023 (2023Q4). The results of the study indicate that, in the long term, the demand for money in Indonesia is influenced by two main determinants: Gross Domestic Product (GDP) and Financial Institution Depth (FID). In the short term, the significant determinants include interest rates, inflation rates, GDP lag, FID lag, and electricity access. The forecast results suggest that the demand for money in Indonesia is projected to experience positive growth, reaching IDR 16,855,845 billion by 2033. This finding underscores the continued importance of physical currency in the Indonesian economy. Based on these results, this study serves as a guideline for policymakers in managing the demand for money by considering the variables that can either increase or decrease this demand. The positive forecast for the demand for money also highlights its significant role in supporting a stable Indonesian economy in the future. Full article

Highly Urbanized Cities (HUCs) in the Philippines were at the forefront of public health surveillance and response during the COVID-19 pandemic. With the rapid spread of COVID-19 to Philippine cities, local government units continuously assessed, adapted, and implemented public health interventions (PHIs) and depended on available open-source government data (OSGD). This study consolidated PHIs in selected HUCs in the Philippines using high-quality OSGD to create a timeline of interventions and document good practices in local COVID-19 control. OSGD resources were collected from February 2020 to January 2023, and the data quality of OSGD was evaluated using the Journal of the American Medical Association (JAMA) benchmarks. A total of 180 metadata sources that met at least two core standards (Authorship and Currency) were included in the analysis. COVID-19 control strategies were analyzed vis-à-vis the rise of COVID-19 cases and types of PHIs, including the control of imported cases, case management, contact management, behavioral modification, and pharmaceutical intervention. Travel bans and hard lockdowns in Luzon early in the pandemic delayed the introduction of COVID-19 to other parts of the country. Good practices of LGUs for local COVID-19 control, such as quarantine passes, curfews and liquor bans, using QR-based contact tracing, massive community testing in high-risk communities, and free public swabbing centers, were implemented to slow down the local spread of COVID-19. With the evolving scenarios in city-level COVID-19 epidemics, local risk assessments based on available OSGD drove the adoption of relevant and innovative control strategies in HUCs in the Philippines. Lessons learned must be integrated into epidemic preparedness and response programs against future emerging or re-emerging infectious diseases. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

Improvements in quality of life, new technologies and population growth have significantly increased energy consumption in Brazil and around the world. The Paris Agreement aims to limit global warming and promote sustainable development, making green hydrogen a fundamental option for industrial decarbonization. Green hydrogen, produced through the electrolysis of water using renewable energy, is gaining traction as a solution to reducing carbon emissions, with the global hydrogen market expected to grow substantially. This study applies the ${\rho }_{DCCA}$ method to evaluate the cross-correlation between the green hydrogen market and various financial assets, including the URTH ETF, Bitcoin, oil futures, and commodities, revealing some strong positive correlations. It highlights the interconnection of the green hydrogen market with developed financial markets and digital currencies. The cross-correlation between the green hydrogen market and the index representing global financial markets presented a value close to 0.7 for small and large time scales, indicating a strong cross-correlation. The green hydrogen market and Bitcoin also presented a cross-correlation value of 0.4. This study provides valuable information for investors and policymakers, especially those concerned with achieving sustainability goals and environmental-social governance compliance and seeking green assets to protect and diversify various traditional investments. Full article

This article offers a clear and approachable introduction to the evolving landscape of money and the frictions developing between traditional government control and decentralized finance (DeFi). Tailored for readers with a basic awareness of cryptocurrency but limited familiarity with its broader implications, the article demystifies DeFi by explaining its core concepts including blockchain, Centralized Bank Digital Currencies (CBDCs), and the historical role of government regulation of money through central banking. Against this backdrop, it examines the transformative potential of DeFi, emphasizing the growing tension between the centralized authority of governments and the decentralized ideals driving this new financial model. While governments seek to maintain stability and control, individuals increasingly gravitate toward the more affordable, efficient, and inclusive solutions promised by DeFi. Designed to empower readers with a better grasp of the forces shaping the future of finance, this article underscores the importance of understanding the delicate interplay between governmental oversight and decentralized innovation. As the digital economy expands, this dynamic struggle will influence not only economic policies but also personal financial choices and access to resources. Full article

This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations. Full article