Search Results (62)

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article

This paper investigates systemic tail dependence within the crypto-asset ecosystem by examining interconnectedness across eight major tokens spanning Layer 1 cryptocurrencies, DeFi tokens, stablecoins, and infrastructure/governance assets. We employ a novel partial correlation-based network framework and quantile-specific connectedness measures to examine how co-movement patterns evolve under normal and extreme market conditions from September 2021 to March 2025. Unlike conventional correlation or variance decomposition approaches, our methodology isolates direct, tail-specific transmission channels while filtering out standard shocks. The results indicate strong asymmetries in dependence structures. Systemic risk intensifies during adverse tail events, particularly around episodes such as the Terra/Luna crash, the USDC depeg, and Bitcoin’s 2024 halving cycle. Our analysis shows that ETH, LINK, and UNI are key assets in spreading losses when the market falls. In contrast, the stablecoin DAI tends to absorb some of the stress, helping reduce risk during downturns. These results indicate critical contagion pathways and suggest that regulation targeting protocol-level transparency, liquidity provisioning, and interoperability standards may reduce amplification mechanisms without eliminating interdependence. Our findings contribute to the emerging literature on crypto-systemic risk and offer actionable insights for regulators, DeFi protocol architects, and institutional investors. In particular, we advocate for the incorporation of tail-sensitive network diagnostics into real-time monitoring frameworks to better manage asymmetric spillover risks in decentralized financial systems. Full article

The financial markets are highly influential and any change in the economy can be reflected in stock prices and thus have an impact on stock indices. The relationship between stock indices and the way they are affected by extreme phenomena is important for defining diversification strategies and analyzing market maturity. The purpose of this study is to examine the interdependence relationships between the main Euronext stock indices and any changes caused by an extreme event—the COVID-19 pandemic. Copula models are used to estimate the dependence relationships between stock indices pairs after estimating ARMA-GARCH models to remove the autoregressive and conditional heteroskedastic effects from the daily return time series. The financial interdependence structures show a symmetric relationship of influence between the indices, with the exception of the CAC40/ISEQ pair, where there was financial contagion. In the case of the AEX/OBX pair, the dynamics of dependence may have changed significantly in response to the pressure of the pandemic. On the other hand, the dominant influence of the CAC40 before and the AEX after the pandemic confirms that the size and age of these indices give them a benchmark position in the market. Finally, with the exception of the AEX/OBX and CAC40/ISEQ pairs, the interdependencies between the stock indices decreased from the pre- to the post-pandemic sub-period. This result suggests that the COVID-19 pandemic has weakened the correlation between the markets, making them more mature and independent, and less risky for investors. Full article

This study investigates the complexity, efficiency, and sectoral interdependencies of the S&P Global BMI indices during critical global events, including the COVID-19 pandemic and the Russia–Ukraine war. The analysis is conducted in three dimensions: (1) evaluating market efficiency using permutation entropy and the Fisher information measure, (2) exploring sectoral alignments through clustering techniques (hierarchical and k-means clustering), and (3) assessing the influence of geopolitical risk using Multifractal Detrended Cross-Correlation Analysis (MFDCCA). The results highlight significant variations in informational efficiency across sectors, with Utilities and Consumer Staples exhibiting high efficiency, while Emerging Markets and Financials reflect lower efficiency levels. Temporal analysis reveals widespread efficiency declines during the pandemic, followed by mixed recovery patterns during the Ukraine conflict. Clustering analysis uncovers dynamic shifts in sectoral relationships, emphasizing the resilience of defensive sectors and the unique behavior of Developed BMI throughout crises. MFDCCA further demonstrates the multifractality in cross-correlations with geopolitical risk, with Consumer Staples and Energy showing stable persistence and Information Technology exhibiting sensitive complexity. These findings emphasize the adaptive nature of global markets in response to systemic and geopolitical shocks, offering insights for risk management and investment strategies. Full article

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes. Full article

This study evaluates the dynamic risk spillovers and interconnectedness of environmental, social, and governance exchange-traded funds (ESG-ETFs) markets during two significant geopolitical conflicts, the Israel–Palestine and the Russia–Ukraine conflicts, alongside an extended analysis of the full period from July 2020 to October 2024. We investigate how crises transmit risks to the market by using the Total Connectedness Index (TCI) and net spillover measures. Our findings reveal a consistently high level of market interdependence. TCI values rose from 65.71% during the Israel–Palestine conflict to 67.28% in the full sample, indicating intensified risk sharing among markets as crises evolve. The markets “Deka MSCI World Climate Change ESG UCITS ETF (D6RP)” and “Amundi MSCI World SRI Climate Net Zero Ambition PAB UCITS ETF EUR Acc (XAMB)” emerge as prominent risk transmitters across all periods, actively spreading volatility throughout the system in both the crisis. In contrast, the markets “Amundi MSCI World Climate Transition CTB—UCITS ETF DR—EUR-C (LWCR)” and “Franklin STOXX Europe 600 Paris Aligned Climate UCITS ETF (PARI)” are primary risk receivers, absorbing a substantial portion of the instability in the Israel–Palestine and Russia–Ukraine conflicts. These dynamics underscore the shifting roles of financial markets during prolonged geopolitical tensions. These findings highlight the necessity of monitoring global markets, particularly during geopolitical shocks, to mitigate systemic risk and effectively navigate financial instability. Full article

Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and digital interactions amplify the visibility and potential impact of reputational issues in the context of electronic commerce, it is essential to thoroughly investigate environmental factors to achieve a comprehensive understanding of reputational risk. However, measuring and evaluating their influence on reputational risk is challenging due to their inherent connection to human perception. This study aims to explore the factors influencing reputational risk of financial organizations to mitigate potential reputational losses by addressing uncertainties associated with concepts such as vagueness. The employed methodology integrates the Decision-Making Trial and Evaluation Laboratory and Fuzzy Cognitive Map techniques using linguistic fuzzy terms. This approach focuses on both the direct effects of factors on reputational risk and the indirect effects arising from interdependencies between factors. Linguistic fuzzy variables enable us to consider the hesitation of the experts and the vagueness of human judgment. To validate the results, factors are also weighted using the fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method. The most influential factors identified by both methods are market value, revenue, risk culture, shareholder value, firm performance, reputation awareness, and return on equity. Additionally, factors affecting other factors include firm performance, revenue, and growth opportunities. Full article

This study investigates the financial interconnections among Real Estate Investment Trusts (REITs), sukuk (Islamic bonds), and oil in Gulf Cooperation Council (GCC) nations. The study sample comprises S&P GCC Composite Equity Real Estate Investment Trusts (REITs) Shariah, the S&P GCC Bond and Sukuk Index, and the OPEC crude oil basket on a daily basis. The duration of coverage spans from 2014 until the beginning of 2024. The TVP-VAR methodology is utilized to examine the interrelationship among the assets. The results indicate that Real Estate Investment Trusts (REITs) and oil are sources of volatility transmission, whereas sukuk is a recipient of volatility within the network. Examining the net pairwise directional linkages of two assets, namely REITs and oil markets, reveals that they transfer their volatility to the sukuk market. Moreover, a reciprocal relationship exists between REITs and oil regarding volatility spillover. It means that REITs act as transmitters to the oil markets during specific periods, while the influence is reversed at other times. This study implies that portfolio managers and investors can discern the volatility patterns of assets in order to enhance their risk-management techniques. For policymakers, comprehending the interdependence of certain asset classes provides valuable knowledge for formulating regulations that might stabilize the financial system and foster economic growth. From a research and academic perspective, this study enhances understanding of the interconnections between different financial asset classes and pricing dynamics in financial markets. Full article

This paper investigates the impact of natural gas pipeline congestion on the integrated gas–electricity market in Peru, focusing on short-term market dynamics. By simulating congestion by reducing the primary natural gas pipeline’s capacity, the study reveals significant patterns in production costs and load flows within the electrical network. The research highlights the critical interdependencies between natural gas and electricity systems, emphasizing how constraints in one network can directly affect the other. The findings underscore the importance of coordinated management of these interconnected systems to optimize economic dispatch and ensure the reliability of both gas and electricity grids. The study also proposes strategic public policy interventions to mitigate the financial and physical impacts of pipeline congestion, contributing to more efficient and resilient energy market operations. Full article

This research aims to explore market mavericks by redefining sales velocity and profit surge in today’s dynamic business environment in emerging economies. The study focuses on the interplay between Sales Excellence (SE), Sales Capability (SC), Market Alignment (MA), Strategic Responsiveness (SR), and Dynamic Sales Management (DSM). Data from 180 companies (2021–2023), provided by financial leaders, were analyzed using SPSS (23.0) and AMOS (23.0) software. The analysis employed exploratory factor analysis (EFA), reliability analysis, and confirmatory factor analysis (CFA). The results highlight the critical role of these factors in shaping market mavericks and their significant impact on sales and profits in emerging economies. Specifically, SE enhances sales and profits when supported by effective strategies, SC drives organizational change by aligning service quality with SE, and MA drives sales velocity and profit surges through accurate forecasting. SR positively influences sales results by aligning sales with corporate strategy, while DSM is critical for motivating salespeople and shows strong links to SC and SR for successful adaptation in a dynamic business environment. The study reveals the interdependence of these factors and emphasizes the need for seamless integration and coordination to drive effective organizational change. These findings have significant implications for corporations seeking to improve their sales strategies and achieve sustainable growth in a rapidly evolving marketplace in emerging economies. This research explores market mavericks, redefines sales velocity and profit surge, and provides valuable insights into the critical factors shaping market mavericks and their impact on sales and profits. It offers guidance for organizations seeking sustainable growth. Full article

This research paper aims to investigate the interdependent influences of barriers to implementing reverse logistics in the broad spectrum of processing activities in the conditions of an emerging economy. An effort was made to approach these barriers (i.e., organizational and management barriers, technical and technological barriers, and economic, financial, and market barriers) based on the relevant literature, predominant attitudes, and experts’ opinions, thus contributing to the body of knowledge in this domain. Determining the intensity of interdependent influences and the importance of barriers for implementing reverse logistics was performed to determine the most important (key) barriers that can be practically applied as guidelines for decision making. The Fuzzy DEMATEL method was used to determine the intensity of these influences on a sample of manufacturing companies in the Republic of Serbia. The results indicate that the most critical barriers to the successful implementation of reverse logistics are a lack of management support and cooperation with scientific institutions and professional associations to acquire knowledge and follow trends in the field. Full article

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises. Full article

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection. Full article

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. Full article

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes. Full article