Search Results (29)

We examine whether speculative bubbles in shadow banking institutions contribute to the buildup and materialization of systemic risk. Using the Phillips–Shi–Yu (BSADF) bubble detection methodology and market-based systemic risk measures (ΔCoVaR and Marginal Expected Shortfall), we analyze daily data for 17 publicly listed U.S. shadow banking firms over the period 2010–2026. We document a pronounced pro-cyclical measurement puzzle. During bubble periods, firms exhibit higher market exposure and greater tail risk—Beta increases by 4.9% and Expected Shortfall by 7.9%—yet widely used systemic risk measures decline, with ΔCoVaR falling by 6.6%. This pattern suggests that conventional systemic risk metrics may underestimate vulnerabilities during speculative expansions. However, when bubbles burst, systemic risk materializes rapidly. During burst windows, ΔCoVaR increases by 7.9% and MES by 8.6%, indicating that vulnerabilities accumulated during bubble phases translate into significant systemic spillovers once speculative dynamics collapse. Our findings highlight a pro-cyclical bias in commonly used systemic risk indicators: these measures capture realized financial stress but fail to detect the buildup of fragility during expansion phases. Monitoring bubble dynamics in shadow banking may therefore provide valuable complementary signals for macroprudential surveillance. Full article

In the context of green transformation, climate change and its economic implications are attracting increasing attention. Based on the Trade-off Theory framework, this study examines how climate risk affects firms’ shadow banking activities in emerging markets. This study focuses on emerging market economies, using a panel dataset of Chinese A-share non-financial listed firms from 2007 to 2023 to systematically examine the relationship between climate risk and shadow banking activities, that is, financing conducted outside the formal banking system. The empirical findings reveal that climate risk significantly dampens the shadow banking activities of non-financial firms. Further mechanism analysis suggests that this effect operates through two key channels: the weakening of corporate resilience and adjustments in capital structure decisions. Moreover, the analysis uncovers heterogeneous impacts of climate risk on shadow banking, depending on the quality of information disclosure, industry characteristics, and the degree of financing constraints. This research provides new insights into the evolution of corporate financial behavior under climate risk and offers empirical evidence to support firms in optimizing their financial strategies and enhancing their financial risk management capabilities. Full article

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the debate in the context of South Africa and uses the quantile–frequency connectedness approach to examine static and dynamic systemic spillover between the US shadow short rate (SSR) and South African equity, bond and currency markets between 1 December 2019 and 2 March 2023. The findings from the static analysis reveal that systemic connectedness is concentrated at their tail-end quantile distributions and US monetary policy plays a dominant role in transmitting these systemic shocks, albeit these shocks are mainly high frequency with very short cycles. However, the dynamic estimates further reveal that US monetary policy exerts longer-lasting spillover shocks to South African financial markets during periods corresponding to FOMC announcements of quantitative ‘easing’ or ‘tapering’ policies. Overall, these findings are useful for evaluating the effectiveness of the Reserve Bank’s macroprudential policies in ensuring market efficiency, as well as for enhancing investor decisions, portfolio allocation and risk management. Full article

This study explores the drivers and evolution of the shadow economy in Bosnia and Herzegovina—a transitional, post-conflict country facing persistent institutional fragility. Using the Multiple Indicators and Multiple Causes (MIMIC) model, an extension of Structural Equation Modeling, the paper estimates the size and dynamics of the shadow economy from 1996 to 2022. The model integrates macroeconomic indicators (employment rate, GDP per capita, tax revenues) and institutional variables (rule of law, control of corruption), with data primarily sourced from the World Bank. The results show that institutional quality, tax burden, and labor market conditions are significant determinants of the informal sector. The model demonstrates strong statistical validity (CFI = 0.986, RMSEA = 0.05), supported by robustness checks including unit root tests, structural break analysis, and the exclusion of controversial benchmarking methods. The shadow economy responds markedly to major shocks such as the 2008 global financial crisis and the 2014 floods. Findings provide valuable policy insights: strengthening institutions, simplifying tax systems, and encouraging formal labor market participation can significantly reduce informality. The study supports evidence-based reforms to enhance transparency, resilience, and sustainable development in Bosnia and Herzegovina. Full article

Sinkholes are significant geohazards in karst regions that pose risks to landscapes and infrastructure by disrupting geological stability. Usually, sinkholes are mapped by field surveys, which is very cost-intensive with regard to vast coverages. One possible solution to derive sinkholes without entering the area is the use of high-resolution digital terrain models, which are also expensive with respect to remote areas. Therefore, this study focusses on the mapping of sinkholes in arid regions from open-access remote sensing data. The case study involves data from the Sentinel missions over the Mangystau region in Kazakhstan provided by the European Space Agency free of cost. The core of the technique is a multi-scale curvature filter bank that highlights sinkholes (and takyrs) by their very special illumination pattern in Sentinel-2 images. Marginal confusions with vegetation shadows are excluded by consulting the newly developed Combined Vegetation Doline Index based on Sentinel-1 and Sentinel-2. The geospatial analysis reveals distinct spatial correlations among sinkholes, takyrs, vegetation, and possible surface discharge. The generic and, therefore, transferable approach reached an accuracy of 92%. However, extensive reference data or comparable methods are not currently available. Full article

Climate risks are increasingly shaping corporate strategies, raising important considerations for sustainability. This study explores the impact of climate risks on the shadow banking activities of non-financial firms, examining how these risks influence sustainable financial practices. Analyzing a sample of Chinese listed firms from 2010 to 2022, this study finds that climate risks are negatively associated with shadow banking activities, reflecting a shift towards more sustainable financial management. This finding remains robust through various robustness checks and tests for endogeneity. Heterogeneity results indicate that the reduction in shadow activities due to climate risks is more pronounced in firms with higher dependence on external financing and weaker profitability. Mechanism results suggest that climate risks amplify cross-sectional risks for firms, increasing risk sources. Simultaneously, companies enhance their preference for holding cash to address potential risks. The combined effect of these factors leads to a decrease in shadow banking activities among non-financial firms, aligning with a shift towards sustainability. This study provides new insights into understanding how climate risks drive sustainable financial decision-making and enriches the research on the determinants of firm financialization. Full article

After the 2007 financial crisis, many central banks adopted policies to lower their interest rates; the dynamics of these rates cannot be captured using classical models. Recently, Meucci and Loregian proposed an approach to estimate nonnegative interest rates using the inverse-call transformation. Despite the fact that their work is distinguished from others in the literature by their consideration of practical aspects, some technical difficulties still remain, such as the lack of analytic expression for the zero-coupon bond (ZCB) price. In this work, we propose novel approximate closed-form solutions for the ZCB price in the zero lower bound (ZLB) framework, when the underlying shadow rate is assumed to follow the classical one-factor Vasicek model. Then, a filtering procedure is performed using the Unscented Kalman Filter (UKF) to estimate the unobservable state variable (the shadow rate), and the model calibration is proceeded by estimating the model parameters using the Particle Swarm Optimization (PSO) algorithm. Further, empirical illustrations are given and discussed using (as input data) the interest rates of the AAA-rated bonds compiled by the European Central Bank ranging from 6 September 2004 to 21 June 2012 (a period that concerns the ZLB framework). Our approximate closed-form solution is able to show a good match between the actual and estimated yield-rate values for short and medium time-to-maturity values, whereas, for long time-to-maturity values, it is able to estimate the trend of the yield rates. Full article

As one of the world’s largest economies and the biggest emitter of greenhouse gases, China plays a critical role in global environmental management. As China emphasizes new quality productive forces, understanding how green finance can enable green innovation quality (GIQ) is essential for projecting China’s influence in the sustainable development of the global ecological environment. This paper sets up a quasi-natural experiment using the Green Credit Policy (GCP) to examine the impact of green financial credit allocation on the enterprises’ GIQ. The findings demonstrate that the GCP has the potential to improve the GIQ of the green credit-restricted industries, compared to non-green credit-restricted ones. It is worth noting that as China speeds up its industrial digital transformation and productivity improvement, green financial credit allocation can elevate the digitization level and total factor productivity of green credit-restricted industries, leading to a higher GIQ by curbing corporate shadow banking. Further research shows that fintech and financial regulation can strengthen the positive influence of the GCP on GIQ. Moreover, regional intellectual property protection has a beneficial synergistic effect in combination with the policy. Full article

The shadow economy’s size and impact remain subjects of extensive research and debate, holding significant implications for economic policy and social welfare. In Lebanon, the ongoing crisis since 2019 has exacerbated severe economic challenges, with the national currency’s collapse, bank crisis, and foreign reserve deficits. The World Bank reports Lebanon’s financial deficit surpassed $72 billion, three times the GDP in 2021. Despite a drastic decline in GDP, imports have surged to near-pre-crisis levels, exacerbating economic woes and indicating a constant outflow of foreign currencies. Considering such contracting facts, this paper aims to investigate global factors influencing the shadow economy and discern their manifestations in Lebanon during financial crises. Our methodology involves a comprehensive literature review, alongside a case study approach specific to Lebanon. This dual-method strategy ensures a detailed understanding of the shadow economy’s impact and the development of actionable insights for policy and economic reform. Through this approach, we seek to contribute to a nuanced understanding of Lebanon’s economic landscape and provide valuable guidance for policy decisions aimed at reducing corruption, promoting transparency, and fostering a robust formal economy. The increase in the shadow economy raises the formal economy risk, as resources and activities diverted to informal channels hinder the growth and stability of the official economic sector. Although focusing on Lebanon, this analysis deepens the comprehension of the economic landscape and provides valuable guidance for policymakers, researchers, and stakeholders, aiming to address the root causes of informal economic activities and promote sustainable growth in developing countries in general. Full article

Shadow banking is a main way for the financial market to serve the real economy today, and this process is closely related to systemic risk. This study examines the impact of shadow banking associated with sustainable development in China’s banking on systemic risk. We analyze the data obtained from a rich sample of 31 listed commercial banks in China and shadow banking represented by wealth management products (WMPs) by constructing a dynamic complex interbank network model. The results show that the risks and vulnerabilities generated by shadow banking spread out through the interbank network and cause systemic risk to increase. The effect operates through increasing the number of default banks, reducing banks’ survival rate and profit, and forcing central bank bailout funds expansion. However, it has a positive impact in terms of augmenting liquidity and enhancing investment opportunities. Furthermore, the variability in the influence of different categories of shadow banking is assessed, emphasizing that short-term shadow banking exerts a more pronounced impact on systemic risk. In addition, the heterogeneity of the shadow banking effect on different types of commercial banks is explored, revealing that local and rural commercial banks experience a more conspicuous effect compared to state-owned and joint-stock banks. Our findings highlight that improving external supervision, promoting financial internal governance, and constraining credit linkages are vital for alleviating the increase in risks in shadow banking and maintaining the sustainable development of banking. Full article

This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking. Full article

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced bibliometric methods, we explored the global network of publications, identifying key works, influential authors, and the evolution of research over time. The results of the bibliometric analysis have highlighted an annual growth rate of 22.05% in publications related to the topics of shadow banking and financial contagion, illustrating researchers’ interest and the dynamic nature of publications over time. Additionally, significant increases in scientific production have been recorded in recent years, reaching a total of 178 articles published in 2022. The most predominant keywords used in research include “systemic risks”, “risk assessment”, and “measuring systemic risk”. The thematic evolution has revealed that over time, the focus on fundamental concepts used in analyzing these two topics has shifted, considering technological advancements and disruptive events that have impacted the economic and financial system. Our findings provide a detailed insight into the progress, gaps, and future directions in understanding the complex interplay of shadow banking and financial contagion. Our study represents a valuable asset for researchers, practitioners, and policymakers with a keen interest in understanding the dynamics of these critical components within the global financial system. Full article

In this paper, we introduce Sonar image Augmentation with Cut and Paste based DataBank for semantic segmentation (SACuP), a novel data augmentation framework specifically designed for sonar imagery. Unlike traditional methods that often overlook the distinctive traits of sonar images, SACuP effectively harnesses these unique characteristics, including shadows and noise. SACuP operates on an object-unit level, differentiating it from conventional augmentation methods applied to entire images or object groups. Improving semantic segmentation performance while carefully preserving the unique properties of acoustic images is differentiated from others. Importantly, this augmentation process requires no additional manual work, as it leverages existing images and masks seamlessly. Our extensive evaluations contrasting SACuP against established augmentation methods unveil its superior performance, registering an impressive 1.10% gain in mean intersection over union (mIoU) over the baseline. Furthermore, our ablation study elucidates the nuanced contributions of individual and combined augmentation methods, such as cut and paste, brightness adjustment, and shadow generation, to model enhancement. We anticipate SACuP’s versatility in augmenting scarce sonar data across a spectrum of tasks, particularly within the domain of semantic segmentation. Its potential extends to bolstering the effectiveness of underwater exploration by providing high-quality sonar data for training machine learning models. Full article

The present work is amongst the few that attempt to critically assess the maturity of Business Model (BM) and strategy disclosures of listed firms under the shadow of the new EU reporting directive, the Corporate Sustainability Reporting Directive (CSRD). The novel Practices Evaluation Approach (PEA), developed recently by the Project Task Force on Reporting of Non-Financial Risks and Opportunities (PTF-RNFRO), offers the evaluation framework for this assessment. The PEA delineates and evaluates the maturity of BM and strategy disclosures against qualitative characteristics and content elements drawn from well-accepted, financial and non-financial, reporting frameworks, standards and directives (including the CSRD). Therefore, the PEA provides the advantage of a contemporary and integrated/holistic assessment tool. Specifically, the following seven evaluation criteria are used for the assessment: clarity and comprehensiveness of the overall BM, strategy disclosure, disclosure of the BM’s potential across-time horizons and its dependencies, impacts on sustainability issues, material sustainability issues that are likely to affect the company’s performance, the BM’s exposure to sustainability risks and sustainability opportunities, and sustainability strategy, targets, KPIs and their monitoring and progress. The analysis covered 30 CSR/sustainability reports and connected documents of listed companies operating in 6 key sectors of the Greek economy, i.e., information technology, construction, tourism and transportation, cosmetics, banking and energy. The results of our analysis offer evidence that BM reporting is not holistically developed (i.e., critical components are missing), and the level of development varies across the examined sectors. Moreover, sustainability risks are more stressed, in relevance to opportunities, whilst positive (rather than negative) impacts are mainly disclosed. Also, the quantification of sustainability risks and opportunities does not appear frequently, whilst the interconnections between sustainability strategy and companies’ financial objectives is relatively restricted. The paper concludes by pointing out some critical hints useful for enhancing the maturity of BM and strategy disclosures. Full article

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body–tag–anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error. Full article