Search Results (81)

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

This study investigates the relationship between sovereign credit rating transitions and domestic equity market performance, focusing on Greece from 2004 to 2024. Although credit ratings are central to sovereign risk assessment, their immediate influence on financial markets remains contested. This research adopts a multi-method analytical framework combining algebraic combinatorics and time-series econometrics. The methodology incorporates the construction of a directed credit rating transition graph, the partially ordered set representation of rating hierarchies, rolling-window correlation analysis, Granger causality testing, event study evaluation, and the formulation of a reward matrix with optimal rating path optimization. Empirical results indicate that credit rating announcements in Greece exert only modest short-term effects on the Athens Stock Exchange General Index, implying that markets often anticipate these changes. In contrast, sequential downgrade trajectories elicit more pronounced and persistent market responses. The reward matrix and path optimization approach reveal structured investor behavior that is sensitive to the cumulative pattern of rating changes. These findings offer a more nuanced interpretation of how sovereign credit risk is processed and priced in transparent and fiscally disciplined environments. By bridging network-based algebraic structures and economic data science, the study contributes a novel methodology for understanding systemic financial signals within sovereign credit systems. Full article

Many of the existing theoretical and empirical studies ignore the two-way relationship between a sovereign’s credit risk and economy. To address this gap, we develop a theoretical model that incorporates the feedback effects of sovereign-debt credit risk on a country’s economy and then provide empirical implications. The model links the risks of sovereign debt and economic fundamentals through a two-way transmission mechanism. In doing so, it demonstrates how economic-fundamentals-driven sovereign-debt credit risk can have a significant impact on economic fundamentals through a feedback effect that has the potential to significantly raise the sensitivity of a country’s economic performance to shocks from both the credit risk associated with sovereign debt and economic fundamentals. The outcomes of the theoretical model are then verified by empirically testing the feedback effects using a structural equation model (SEM) framework on data covering sovereign debt defaults worldwide. We demonstrate how disregarding feedback effects may result in information that is insufficient and less helpful to public-debt-management policymakers. Full article

The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (RNN, LSTM, BiLSTM, and GRU) to predict CDS-based implied default probabilities. We compare the predictive accuracy of the tested models with the results showing that Linear Regression outperforms all other techniques, while deep learning architectures, such as RNN and GRU, demonstrate a competitive performance. To validate the sovereign credit risk prediction, we use the forecasted implied default probability from the Linear Regression model to determine the corresponding forecasted implied rating according to the Thomson Reuters StarMine Sovereign Risk model. The results reveal significant differences in the perceived creditworthiness of Egypt, Morocco, and Saudi Arabia, reflecting each country’s economic fundamentals and their ability to manage global shocks, particularly those related to the Russo-Ukrainian war. Specifically, Egypt is perceived as the most vulnerable, Morocco occupies an intermediate position, and Saudi Arabia is seen as having a low credit risk. This study provides valuable managerial insights by enhancing tools for the sovereign credit risk analysis, offering reliable decision-making in volatile global markets. The alignment between forecasted ratings and default probabilities underscores the practical relevance of the results, guiding stakeholders in effectively managing credit risks amidst economic uncertainty. Full article

This study explores the nuanced relationship between biodiversity and sovereign credit ratings, underscoring the link between environmental sustainability and economic resilience. As credit rating methodologies increasingly incorporate Environmental, Social, and Governance (ESG) dimensions alongside traditional macroeconomic indicators, biodiversity has emerged as a vital factor influencing sovereign creditworthiness. Drawing on a panel dataset of 62 countries—representing 91% of the global GDP and 81% of the world’s greenhouse gas emissions—from 2001 to 2021, the research utilizes advanced econometric techniques, including the panel Generalized Method of Moments (GMM) and panel quantile regression. The GMM analysis indicates that higher biodiversity levels are generally associated with a decline in credit ratings. However, the quantile regression provides a more differentiated view, revealing that biodiversity’s impact varies by a country’s existing credit standing. Specifically, nations with lower credit ratings tend to benefit from richer biodiversity, while countries with higher credit ratings show a modest negative association—reflecting structural and institutional differences. Robustness checks confirm these results, highlighting the relevance of biodiversity indicators such as the Red List Index in credit evaluations. The findings support the integration of biodiversity into sovereign risk assessments to enhance the alignment of financial systems with long-term ecological and economic sustainability goals. Full article

Credit risk assessment plays a critical role in financial risk management, focusing on predicting borrower default to minimize losses and ensure compliance. This study systematically reviews 23 empirical articles published between 2019 and 2023, highlighting the integration of machine learning and optimization techniques, particularly bio-inspired metaheuristics, for feature selection in individual credit risk assessment. These nature-inspired algorithms, derived from biological and ecological processes, align with bio-inspired principles by mimicking natural intelligence to solve complex problems in high-dimensional feature spaces. Unlike prior reviews that adopt broader scopes combining corporate, sovereign, and individual contexts, this work focuses exclusively on methodological strategies for individual credit risk. It categorizes the use of machine learning algorithms, feature selection methods, and metaheuristic optimization techniques, including genetic algorithms, particle swarm optimization, and biogeography-based optimization. To strengthen transparency and comparability, this review also synthesizes classification performance metrics—such as accuracy, AUC, F1-score, and recall—reported across benchmark datasets. Although no unified experimental comparison was conducted due to heterogeneity in study protocols, this structured summary reveals consistent trends in algorithm effectiveness and evaluation practices. The review concludes with practical recommendations and outlines future research directions to improve fairness, scalability, and real-time application in credit risk modeling. Full article

Counterfeit products cause financial losses for both the manufacturer and the enduser; e.g., fake foods and medicines pose significant risks to the public’s health. Moreover, it is challenging to ensure trust in a product’s supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection methods are expensive and need to be more scalable, whereas a unified traceability system for packaged products is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). The TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers distributed nodes, to verify them without a centralized client–server authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust, with the use of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrated its superiority in execution time and number of successful transactions. Full article

With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless communication networks. The SSFXG framework separates identity management from the service management layer typically controlled by network operators to foster interoperability functionalities with enhanced privacy and trace-resistant assurances in the NextG landscape. The proposed model relies on blockchain technology as an infrastructure to enable single-authority-free service provisioning and boost mutual trust among federated network components. Further, the SSFXG framework facilitates subscribers’ self-sovereignty over their subscription information while ensuring anonymity and enhanced privacy preservation, avoiding unnecessary network activity monitoring or tracking. Preliminary evaluations demonstrated the effectiveness and efficiency of the proposed framework, making it a promising solution for advancing secure and interoperable NextG wireless networks. Full article

Lawful Interception refers to the acquisition of the contents of communications between private individuals or organizations by subjects authorized by law. It involves three actors: the network operator (NO), the Law Enforcement Agency (LEA), and the Law Enforcement Monitoring Facility (LEMF). In the literature, standards and scientific solutions are proposed for the interception procedure and the interaction between the NO and the LEMF. However, no standard has been proposed for the interaction between the LEMF and the LEA. The absence of standards for controlling LEA (or a delegated agency) access to intercepted contents stored by the LEMF is a significant gap that should be overcome. This prevents the implementation of secure, interoperable, and automated procedures, leading to inefficiencies and security risks. In this paper, we propose to cover the above gap by adopting the Self-Sovereign Identity (SSI) paradigm. The adopted research methodology follows a multi-phase approach that includes studying existing solutions, system design, and technical feasibility testing. The study first examines existing standards and identity management frameworks and their limitations. Next, an SSI-based architecture is proposed to manage the interactions between LEA (or a delegated agency) and LEMF. Finally, a proof of concept of the proposed solution written in Python and using the Hyperledger Indy blockchain has been implemented to assess whether our proposal is technically feasible. The proposed solution enhances automation, security, and interoperability in lawful interception. Indeed, it enables machine-readable authorizations, reducing errors and improving efficiency by eliminating manual operations. Additionally, verifiable credentials and decentralized identifiers strengthen security and standardize interactions across jurisdictions, ensuring privacy-preserving identity management. By standardizing interactions between LEA and LEMF, this research contributes to a more secure, privacy-preserving, and legally compliant lawful interception process. Full article

In recent years, with the development of blockchain technology and increased awareness of personal privacy, Self-Sovereign Identity (SSI) has become a hot topic. SSI gives customers more autonomy over their personal information, allowing them to control who can access and use their personal information. This provides customers with higher levels of privacy protection, as their data are no longer controlled by centralized institutions. To address the credit assessment needs of financial institutions, this paper proposes a Customer Self-Sovereign Identity and access-control framework (CSSI) based on SSI technology. Customers can securely store assessable assets and credit data on the blockchain using this framework. These data are then linked to a digital account address. With customer authorization, financial institutions processing loan applications can comprehensively evaluate customers’ repayment capabilities and conduct risk management by accessing this credit data. CSSI assists financial institutions in optimizing complex and repetitive processes involved in customer credit assessment and loan origination through SSI and access control, thereby reducing unnecessary risks. Full article

Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper’s scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions. Full article

Traditional identity management (IdM) solutions based on centralised protocols, such as Lightweight Directory Access Protocol (LDAP) and Security Assertion Markup Language (SAML), are where a central authority manages all the processes. This risks a single point of failure and other vulnerabilities. In response, decentralised techniques like blockchain and decentralised identities (DIDs) are being explored. This review paper performs a comparison of popular decentralised identity management (DIM) protocols, such as self-sovereign identity (SSI), against traditional centralised approaches such as LDAP and SAML. These decentralised identity management systems are being developed, keeping users’ identity data as its highest priority. Additionally, this method eliminates the need for a central authority to manage and secure the system. To further explore the potential of decentralised identity management, this study delves into popular blockchain-based decentralised identity management systems such as uPort, Sovrin, EverID, Blockstack, ShoCard, and Hyperledger Indy. We analyse their underlying principles and compare them with the well-established centralised identity management solutions, focusing on key aspects such as scalability, security, and feasibility. However, despite their benefits and several worthy developments in this field, decentralised approaches are still not widely used. Through this study, we investigate both centralised and decentralised methods and review their strengths and weaknesses. By reviewing multiple research papers, this survey aims to provide an understanding and aid in selecting the most suitable identity management system for different use cases. Full article

Frequent user data breaches and misuse incidents highlight the flaws in current identity management systems. This study proposes a blockchain-based, peer-supervised self-sovereign identity (SSI) generation and privacy protection technology. Our approach creates unique digital identities on the blockchain, enabling secure cross-domain recognition and data sharing and satisfying the essential users’ requirements for SSI. Compared to existing SSI solutions, our approach has the practical advantages of less implementation cost, ease of users’ understanding and agreement, and better possibility of being soon adopted by current society and legal systems. The key innovative technical features include (1) using a zero-knowledge proof technology to ensure data remain “usable but invisible”, mitigating data breach risks; (2) introducing a peer review mechanism among service providers to prevent excessive data requests and misuse; and (3) implementing a comprehensive multi-party supervision system to audit all involved parties and prevent misconduct. Full article

In this paper, we analysed several emerging market (EM) and developed market (DM) sovereign yield curves to identify the proportion of parallel and non-parallel shifts over time. We found that non-parallel shifts are more prevalent in EM due to higher political and economic risks. Key drivers include systemic risk events like wars, debt distress, and pandemics. By backtesting a long butterfly strategy to extract non-parallel risk premia from June 2007 to March 2024, we observed that steeper slopes and greater curvature result in higher returns. We also quantified monetary and fiscal regimes to determine what types of policies are required to extract non-parallel risk premia from these sovereign yield curves. Our research suggests that countries with opposing monetary and fiscal policies possess higher return opportunities whilst countries with complementing policies require tactical butterfly strategies to optimise returns. Full article

This paper examines the time-varying spillover effects and connectedness between the euro and other EU and non-EU currencies after the end of the sovereign-debt crisis. We employ the Quantile Vector Autoregression connectedness approach using intraday data for seven currencies (the euro, the British pound, the Swiss franc, the Polish zloty, the Hungarian forint, the Czech koruna, and the Norwegian krone) spanning from 1 January 2016 to 30 November 2022. The results indicate that, almost in all quantiles, the currencies of Eastern European Group countries (i.e., Czech Republic, Hungary, and Poland) are net contributors of information spillovers to other currencies, while currencies of non-EU countries (Switzerland, UK, and Norway) are net takers. Further, we find that the euro is the highest transmitter of net information spillovers to all other currencies until 2021. Interestingly, after 2021, the euro changes to net information spillover taker from all other currencies; highlighting that external shocks (e.g., COVID-19, the energy crisis) have significant risk spillover effects on the European currency market. Policymakers and market participants could benefit from knowing which currency drives developments to avoid unexpected consequences. Full article