Search Results (161)

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and crude oil futures, EUA futures, and natural gas futures. The study applies two main approaches—a conditional value-at-risk (CVaR)-based relative risk ratio (RRR) analysis and dynamic conditional correlation (DCC-GARCH) modeling—to assess tail risk mitigation and time-varying correlations. The results show that while green assets do not consistently act as safe havens during extreme market downturns, they can reduce the portfolio tail risk beyond certain allocation thresholds. Natural gas futures demonstrate significant volatility but offer diversification benefits when their portfolio weight exceeds 40%. EUA futures, although highly correlated with carbon emissions futures, show limited safe haven behavior. The findings challenge the assumption that green assets inherently provide downside protection and highlight the importance of strategic allocation. This research contributes to the literature by extending safe haven theory to environmental futures and offering empirical insights into the risk dynamics between green and dirty assets. Full article

Background: Street food vending provides vital employment and nutrition in low- and middle-income countries (LMICs), but poor health and safety compliance pose significant public health and business risks. Despite growing policy recognition, the link between hygiene practices and vendor performance remains underexplored. Objective: This integrative review examines the influence of health and safety practices on the business performance of informal street food vendors, with a particular focus on both global and South African contexts. Methods: A total of 76 studies published between 2015 and 2025 were retrieved between June 2024 and May 2025 and analyzed using an integrative review methodology. Sources were identified through five major academic databases and grey literature repositories. Thematic synthesis followed PRISMA logic and was guided by the Health Belief Model (HBM) and Balanced Scorecard (BSC) frameworks. Results: There was a marked increase in publications post-2019, peaking in 2023. Sub-Saharan Africa accounted for the majority of studies, with South Africa (28%) and Ghana (14%) most represented. Among the 76 included studies, the most common designs were quantitative (38%), followed by qualitative (20%), case studies (14%), and mixed-methods (11%), reflecting a predominantly empirical and field-based evidence base. Thematic analysis showed that 26% of studies focused on food safety knowledge and practices, 14% focused on infrastructure gaps, and 13% focused on policy and regulatory challenges. Of the 76 studies included, 73% reported a positive relationship between hygiene compliance and improved business performance (such as customer trust, revenue, and operational resilience), based on vote-counting across qualitatively synthesized results and business outcomes. The review identifies a conceptual synergy between the HBM’s cues to action and the BSC’s customer dimension, highlighting how hygiene compliance simultaneously influences vendor behaviour and consumer trust. Conceptual saturation was observed in themes related to hygiene protocols, consumer trust indicators, and regulatory barriers. Conclusions: Health and safety practices function not only as compliance imperatives but also as strategic assets in the informal food economy. However, widespread adoption is impeded by structural barriers including limited infrastructure, education gaps, and uneven regulatory enforcement. The findings call for context-sensitive policy interventions and public health models that align with vendor realities and support sustainable, safe, and competitive informal food systems. Full article

Maintenance techniques are fundamental in the context of the safe operation of continuous process installations, especially in electrical energy-transmission and/or -distribution substations. The operating conditions of power transformers are fundamental for the safe functioning of the electrical power system. Predictive maintenance consists of periodically monitoring the asset in use, in order to anticipate critical situations. This article proposes a methodology based on data science, machine learning and the Internet of Things (IoT), to track operational conditions over time and evaluate transformer aging. This characteristic is achieved with the development of a synchronization method for different databases and the construction of a model for estimating ambient temperatures using k-Nearest Neighbors. In this way, a history assessment is carried out with more consistency, given the environmental conditions faced by the equipment. The work evaluated data from three power transformers in different geographic locations, demonstrating the initial applicability of the method in identifying equipment aging. Transformer TR1 showed aging of $3.24\times {10}^{-3}\%$, followed by TR2 with $8.565\times {10}^{-3}\%$ and TR3 showing $294.17\times {10}^{-6}\%$ in the evaluated period of time. Full article

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

Exchange rate volatility poses significant challenges for emerging markets, influencing trade balances, inflation, and capital flows. South Africa’s Rand is particularly vulnerable to global risk sentiment, gold price fluctuations, and interest rate differentials, yet prior studies often analyse these factors in isolation. This study integrates them within an autoregressive distributed lag framework, using monthly data from 2005 to 2023 to capture both short-term fluctuations and long-term equilibrium effects. The findings confirm that higher global risk sentiment triggers immediate Rand depreciation, driven by capital outflows to safe-haven assets. Conversely, rising gold prices and favourable interest rate differentials stabilise the Rand, strengthening trade balances and attracting capital inflows. These results underscore the interconnected nature of global financial conditions and exchange rate movements. This study highlights the importance of economic diversification, foreign reserve accumulation, and proactive monetary policies in mitigating currency instability in emerging markets. Full article

With the development of global digital economy and the digital transformation of enterprises, the demand for cross-border cross-domain sharing and circulation of highly sensitive and high-value data assets is becoming more and more obvious. In the process of shared circulation, data assets are faced with some problems, such as unreliable communication network, uncontrollable cloud storage service, untrusted participants and so on, which leads to data tampering, stealing, blocking and tracing back to the source. However, the existing security protection means are difficult to systematically ensure the safe circulation and utilization of data assets in an uncontrolled, high threat and strong confrontation environment. Therefore, this paper establishes a security protection model of data assets in the whole life cycle with cryptography technology as the core, and designs a security technical framework that runs through each link of data asset sharing and circulation. In addition, an architecture design scheme of data asset security sharing and circulation system based on cryptography service technology is proposed, which can systematically solve the security problem of data asset sharing and circulation in uncontrolled environments, and can improve the ability of on-demand deployment, flexible access and dynamic adjustment while maximizing the security of data assets. Full article

Amidst global economic fluctuations and escalating geopolitical risks, gold futures, as a pivotal safe-haven asset, demonstrate price dynamics that directly impact investor decision-making and risk mitigation effectiveness. Traditional forecasting models face significant limitations in capturing long-term trends, addressing abrupt volatility, and mitigating multi-source noise within complex market environments characterized by nonlinear interactions and extreme events. Current research predominantly focuses on single-model approaches (e.g., ARIMA or standalone neural networks), inadequately addressing the synergistic effects of multimodal market signals (e.g., cross-market index linkages, exchange rate fluctuations, and policy shifts) and lacking the systematic validation of model robustness under extreme events. Furthermore, feature selection often relies on empirical assumptions, failing to uncover non-explicit correlations between market factors and gold futures prices. A review of the global literature reveals three critical gaps: (1) the insufficient integration of temporal dependency and global attention mechanisms, leading to imbalanced predictions of long-term trends and short-term volatility; (2) the neglect of dynamic coupling effects among cross-market risk factors, such as energy ETF-metal market spillovers; and (3) the absence of hybrid architectures tailored for high-frequency noise environments, limiting predictive utility for decision support. This study proposes a three-stage LSTM–Transformer–XGBoost fusion framework. Firstly, XGBoost-based feature importance ranking identifies six key drivers from thirty-six candidate indicators: the NASDAQ Index, S&P 500 closing price, silver futures, USD/CNY exchange rate, China’s 1-year Treasury yield, and Guotai Zhongzheng Coal ETF. Second, a dual-channel deep learning architecture integrates LSTM for long-term temporal memory and Transformer with multi-head self-attention to decode implicit relationships in unstructured signals (e.g., market sentiment and climate policies). Third, rolling-window forecasting is conducted using daily gold futures prices from the Shanghai Futures Exchange (2015–2025). Key innovations include the following: (1) a bidirectional LSTM–Transformer interaction architecture employing cross-attention mechanisms to dynamically couple global market context with local temporal features, surpassing traditional linear combinations; (2) a Dynamic Hierarchical Partition Framework (DHPF) that stratifies data into four dimensions (price trends, volatility, external correlations, and event shocks) to address multi-driver complexity; (3) a dual-loop adaptive mechanism enabling endogenous parameter updates and exogenous environmental perception to minimize prediction error volatility. This research proposes innovative cross-modal fusion frameworks for gold futures forecasting, providing financial institutions with robust quantitative tools to enhance asset allocation optimization and strengthen risk hedging strategies. It also provides an interpretable hybrid framework for derivative pricing intelligence. Future applications could leverage high-frequency data sharing and cross-market risk contagion models to enhance China’s influence in global gold pricing governance. Full article

Extreme weather events are the primary driver of environmental, social, and governance (ESG) responsible investment or sustainable stocks, which are gaining popularity worldwide, including in Thailand. Nevertheless, the function of sustainable stocks remains an academic dispute and without satisfactory conclusion for decision-making of Thai investors. Thus, we adopt a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to examine the influence of Thailand sustainability investment on Thailand’s stock market and financial assets. The result indicates that Thailand sustainability investment lacks hedging functions and is classified as a weak safe-haven for consumer product stocks, bitcoin, and Thai baht. Consequently, Thailand sustainability investment provides a better alternative asset for risk diversification, although volatility is low compared to other financial assets and decreases during crises. Investors are advised to diversify their investment risks by adding Thailand sustainability investment to their portfolios during a bearish market. Full article

This paper conducts a comprehensive review of the role of digitalization in railway maintenance management, particularly through the lens of the International Union of Railways (UIC) asset management framework. The study aims to assess how digital technologies such as Big Data, the Internet of Things (IoT), and Artificial Intelligence (AI) serve as enablers for more efficient and effective maintenance practices in the railway sector. By employing a bibliometric analysis, we identify the current trends, challenges, and gaps in the literature concerning the integration of digital tools into maintenance management frameworks. The findings reveal that while digitalization offers significant potential for optimizing maintenance operations and enhancing decision-making processes, its successful implementation requires a more integrated approach that aligns with the strategic goals of railway organizations. This paper also discusses future research directions, emphasizing the need for a global framework incorporating technological advancements and organizational change to achieve sustainable and safe railway operations. Full article

This study examines the volatility and hedging effectiveness of commodities, specifically gold and oil, on the Indian stock market, focusing on both aggregate and sectoral indices. Data have been collected from 1 January 2021 to 31 December 2024 to cover the post-COVID-19 period. Utilizing the Asymmetric Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (ADCC-GARCH) model, we analyze the volatility spillovers and time-varying correlations between commodity and stock market returns. The analysis of spillover connectedness reveals that both commodities exhibit limited and inconsistent hedging potential. Gold demonstrates low and stable spillovers in most sectors, indicating its diminished role as a reliable safe-haven asset in Indian markets. Oil shows relatively higher but volatile spillover effects, particularly with sectors closely tied to energy and industrial activities, reflecting its dependence on external economic and geopolitical factors. This study contributes to the literature by providing a sector-specific perspective on commodity–stock market interactions, challenging conventional assumptions of hedging efficiency of gold and oil. It also emphasizes the need to explore alternative hedging mechanisms for risk management in the post-crisis phase. Full article

Comprehending intermarket relationships among asset classes/commodities and the changing dynamics among the gold, bitcoin, and oil markets under high or low-volatility indexes is now imperative for investors. This paper presents a qualitative study to elicit expert views on the relationships between two major commodities (gold and oil) and bitcoin, specifically emphasizing the pre- and post-COVID-19 era. The thematic analysis of 30 finance experts revealed gold as a safe haven and portfolio diversifier; however, it has lost importance as an inflation hedge post-COVID-19 (2020–2022). Moreover, findings indicated that bitcoin was not a substitute for gold and that there was a positive correlation between gold and oil and the gold volatility index (VIX). Furthermore, there was a negative correlation between the oil VIX and the bitcoin VIX, with no correlation between the gold–bitcoin or oil–bitcoin nexus. These findings are pertinent for investors and scholars in the context of portfolio allocation/portfolio design that comprise these vital asset classes/commodities. Full article

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems. Full article

This paper addresses the challenge of optimizing microgrid sizing to enhance reliability and efficiency in electrical energy supply. A comprehensive framework that integrates multiyear optimization with uncertainty considerations is presented to facilitate optimal microgrid design. The aim is to economically, safely, and reliably supply electrical energy to communities with limited or no access to the main power grid, primarily utilizing renewable sources such as solar and wind technologies. The proposed framework incorporates environmental stochasticity, electrical demand uncertainty, and various electrical generation technologies. Electric power generation models are developed, and a metaheuristic optimization method is employed to minimize total costs while improving power supply reliability. The practical utility of the developed computational tool is emphasized, highlighting its significance in decision-making for microgrid installations. Utilizing real-world data, the approach involves a two-stage process: the first stage focuses on installation decisions, and the second evaluates operational performance using an iterated local search (ILS) optimization algorithm. Additionally, dispatch strategies are implemented to optimize computational time and enable real-time network modeling. The proposed microgrid sizing approach is a valuable asset for optimizing decision-making processes, significantly contributing to extending electricity coverage in non-interconnected zones while minimizing costs and ensuring steadfast reliability. Full article