Immanuel Simon Loblobly, Bramantyo Djohanputro, Wilson Rajagukguk
This study aims to analyze the impact of credit risk on the financial performance of the financing industry in Indonesia using a comparative study approach across the periods before the Covid-19 pandemic (2017–2019), during the pandemic (2020–2021), and after the pandemic (2022–2024). The data used are aggregated secondary industry data sourced from financing institution statistical reports published by the Financial Services Authority (OJK). Credit risk is proxied by the Non-Performing Financing (NPF) ratio, while financial performance is measured using Return on Assets (ROA). The analytical method employed is linear regression with dummy variables and interaction terms to capture differences in impact across crisis periods. The results show that credit risk has a negative and significant effect on the financial performance of the financing industry. In addition, the Covid-19 pandemic is proven to have strengthened the negative impact of credit risk on industry profitability, while the post-pandemic period indicates signs of performance recovery. These findings underscore the importance of adaptive credit risk management and the role of regulatory policies in maintaining the stability of the financing industry during periods of crisis and economic recovery.
Clara Mosso, Stephanie Kampf, Andrea Baudoin Farah
Abstract Wildland–urban interface (WUI) expansion is accelerating in numerous regions around the world due to increasing amenity‐led migration processes, defined as the movement of people seeking higher environmental quality. While WUI areas are complex social–ecological systems requiring holistic planning and management, they are usually approached from wildfire risk mitigation perspectives that overlook broader people–nature relations. We explored the drivers of WUI expansion and the planning and management practices used to address WUI expansion‐related issues through two case studies: the Roaring Fork Valley (US) and San Martín de los Andes (Argentina). Through semi‐structured interviews, we assessed stakeholders' perceptions regarding current WUI planning and management practices and potential strategies for improvement, identifying opportunities to implement transdisciplinary landscape planning and management approaches in these systems. In particular, we highlighted WUI planning and management aspects that go beyond wildfire mitigation, addressing underlying causes of WUI expansion like amenity‐led migration. The drivers of WUI expansion in the study areas mirror those of amenity‐led migration, with a perceived higher life quality as compared to fully urban areas. The interaction of WUI areas with the tourism industry and the consequent rise in real estate prices increases the demand for housing solutions for both low‐ and high‐income sectors, putting pressure on WUI and wildland areas to serve as sites for new construction. This challenges the conception of amenity‐led migration as a process that solely affects wealthy sectors. In addition, different socioeconomic sectors may have varying vulnerability to WUI‐related risks. To improve WUI planning and management approaches, participants emphasized the need to integrate environmental and ecological considerations, such as changes in ecosystem services provision to identify potential sites for future housing development as well as housing density criteria. This latter idea proved contentious, with different stakeholders advocating for low‐density and high‐density developments, options that should be discussed through participatory processes. This study invites reflection on the benefits of transdisciplinarity (knowledge integration, bridging science and practice and enhancing stakeholder engagement) in the context of WUI social–ecological systems, providing insights into more comprehensive and collaborative approaches to WUI planning and management that consider people–nature relations. Read the free Plain Language Summary for this article on the Journal blog.
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions.
Steven Campbell, Natascha Hey, Ciamac C. Moallemi
et al.
Auto-deleveraging (ADL) mechanisms are a critical yet understudied component of risk management on cryptocurrency futures exchanges. When available margin and other loss-absorbing resources are insufficient to cover losses following large price moves, exchanges reduce positions and socialize losses among solvent participants via rule-based ADL protocols. We formulate ADL as an optimization problem that minimizes the exchange's risk of loss arising from future equity shortfalls. In a single-asset, isolated-margin setting, we show that under a risk-neutral expected loss objective the unique optimal policy minimizes the maximum leverage among participants. The resulting design has a transparent structure: positions are reduced first for the most highly levered accounts, and leverage is progressively equalized via a water-filling (or ``leverage-draining'') rule. This policy is distribution-free, wash-trade resistant, Sybil resistant, and path-independent. It provides a canonical and implementable benchmark for ADL design and clarifies the economic logic underlying queue-based mechanisms used in practice. We further study the multi-asset, cross-margin setting, where the ADL problem becomes genuinely multi-dimensional: the exchange must allocate a vector of required reductions across accounts with portfolios exposed to correlated price moves. We show that under an expected-loss objective the problem remains separable across accounts after introducing asset-level shadow prices, yielding a scalable numerical method. We observe that naive gross leverage can be misleading in this context as it ignores hedging within portfolios. When asset prices are driven by a single dominant risk factor, the optimal policy again takes a water-filling form, but now in a factor-adjusted notion of leverage, so that more effectively hedged portfolios are deleveraged less aggressively.
During May 2024, Brazil experienced severe flooding due to heavy rainfall associated with low-pressure systems impacting the state of Rio Grande do Sul. This study analyzed operational VIIRS (Visible Infrared Imaging Radiometer Suite) flood products at the original 375-m spatial resolution and downscaled VIIRS floodwater depth at 30-m resolution derived from the original VIIRS flood products using a downscaling model. The analysis indicated several key findings: (1) The operational VIIRS flood products provided flooding information consistent with the flood extent maps from the Emergency Response Coordination Centre (ERCC); (2) The VIIRS downscaled floodwater depth at 30-m resolution provided high resolution equivalent to SAR (Synthetic Aperture Radar) and allied well with the SAR-based flood extent in the South-eastern region of the state, while maintaining VIIRS’s advantage of large spatial coverage; (3) The 3D maps of VIIRS operational flood products at the original 375-m resolution and the downscaled VIIRS floodwater depth at 30-m resolution revealed that flooding is highly related to surface elevation and slope; and (4) Quantitative analysis of the VIIRS downscaled flood map and SAR flood extents indicates an overall accuracy of 80%, a precision of 61%, a recall of 91%, and an F1 score of 73%.
J. Camilo Gomez-Zapata, Cecilia I. Nievas, Graeme Weatherill
et al.
In seismic risk assessment, expert-elicited collapse factors are used to estimate casualties (injuries and fatalities). However, these approaches often overlook buildings’ pre-existing (pre-earthquake) deterioration. Recent methods estimate human displacement by assuming that occupants of extensively or completely damaged buildings become homeless, without first assessing fatalities, injury levels, and unharmed populations probabilistically. This study presents a method for assessing human displacement that incorporates multi-severity casualty estimates alongside the updated residential capacity of serviceable buildings. It shows that pre-existing damage, represented by hypothetical global damage grades as a proof of concept, can influence casualty estimates and displacement outcomes following an earthquake. The method is applied to exposed buildings and populations in Eastern Sicily and Southern Calabria, Italy. These are spatially aggregated into high-resolution geocells and subjected to hundreds of ground motion fields under a Mw 7.1 earthquake scenario in the Messina Strait, similar to the 1908 event that caused extensive damage and loss of life. For each simulation, damages, non-economic losses (human-centred risk metrics), and economic losses (repair costs) are computed, including costs for portfolios with and without depreciation based on their deterioration levels. This framework aids disaster risk reduction by assessing human displacement and shows the need for emergency shelter facilities.
The extreme cases of risk measures, when considered within the context of distributional ambiguity, provide significant guidance for practitioners specializing in risk management of quantitative finance and insurance. In contrast to the findings of preceding studies, we focus on the study of extreme-case risk measure under distributional ambiguity with the property of increasing failure rate (IFR). The extreme-case range Value-at-Risk under distributional uncertainty, consisting of given mean and/or variance of distributions with IFR, is provided. The specific characteristics of extreme-case distributions under these constraints have been characterized, a crucial step for numerical simulations. We then apply our main results to stop-loss and limited loss random variables under distributional uncertainty with IFR.
This paper introduces an innovative framework for the periodic evaluation of defined-contribution pension funds. The performance of the pension fund is evaluated not only at retirement, but also within the interim periods. In contrast to the traditional literature, we set the dynamic risk measure as the criterion and manage the tail risk of the pension fund dynamically. To effectively interact with the stochastic environment, a model-free reinforcement learning algorithm is proposed to search for optimal investment and insurance strategies. Using U.S. data, we calibrate pension members' mortality rates and enhance mortality projections through a Lee-Carter model. Our numerical results indicate that periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage more risk-seeking behaviors.
Geo-localization is the task of identifying the location of an image using visual cues alone. It has beneficial applications, such as improving disaster response, enhancing navigation, and geography education. Recently, Vision-Language Models (VLMs) are increasingly demonstrating capabilities as accurate image geo-locators. This brings significant privacy risks, including those related to stalking and surveillance, considering the widespread uses of AI models and sharing of photos on social media. The precision of these models is likely to improve in the future. Despite these risks, there is little work on systematically evaluating the geolocation precision of Generative VLMs, their limits and potential for unintended inferences. To bridge this gap, we conduct a comprehensive assessment of the geolocation capabilities of 25 state-of-the-art VLMs on four benchmark image datasets captured in diverse environments. Our results offer insight into the internal reasoning of VLMs and highlight their strengths, limitations, and potential societal risks. Our findings indicate that current VLMs perform poorly on generic street-level images yet achieve notably high accuracy (61\%) on images resembling social media content, raising significant and urgent privacy concerns.
United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation's default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Default Index spreads) and the necessity for asset holders like mutual funds to liquidate assets, including IG credits, to manage margin calls, bolster cash reserves, or meet redemptions. These credit and liquidity premia occur during market drawdowns and tend to move non-linearly with the market. The research herein refers to this non-linearity (during periods of drawdown) as downside convexity, and shows that this market behavior can effectively be captured through a short position established in IG Exchange Traded Funds (ETFs). The following document details the construction of three signals: Momentum, Liquidity, and Credit, that can be used in combination to signal entries and exits into short IG positions to hedge a typical active bond portfolio (such as PIMIX). A dynamic hedge initiates the short when signals jointly correlate and point to significant future hedged return. The dynamic hedge removes when the short position's predicted hedged return begins to mean revert. This systematic hedge largely avoids IG Credit drawdowns, lowers absolute and downside risk, increases annualised returns and achieves higher Sortino ratios compared to the benchmark funds. The method is best suited to high carry, high active risk funds like PIMIX, though it also generalises to more conservative funds similar to DODIX.
Martin Aichele, Igor Cialenco, Damian Jelito
et al.
We develop a statistical framework for risk estimation, inspired by the axiomatic theory of risk measures. Coherent risk estimators -- functionals of P\&L samples inheriting the economic properties of risk measures -- are defined and characterized through robust representations linked to $L$-estimators. The framework provides a canonical methodology for constructing estimators with sound financial and statistical properties, unifying risk measure theory, principles for capital adequacy, and practical statistical challenges in market risk. Numerical illustrations based on simulated and market data demonstrate that coherence of a risk measure does not necessarily carry over to its estimators and show that alternative admissible weight structures within the CRE representation can lead to substantially different capital adequacy outcomes.
K. J. Aladayleh, S. Qudah, José Luis Fuentes Bargues
et al.
Abstract This study used bibliometric analysis to investigate global research trends regarding the effect of COVID-19 risks in sustainable facility management fields. Between 2019 and 2021, the Scopus database published 208 studies regarding the effect of COVID-19 risks on sustainable facility control fields. VOSviewer software was used to analyse the co-occurrence of all keywords, and Biblioshiny software allowed getting the most relevant affiliation using the three-field plot. The results show the contribution by authors from 51 countries, and 73 keywords were identified and organised into six clusters, such as the effect of COVID-19 risks on human health, supply chain in construction projects and industry, disaster risk management in a changing climate, sustainable supply chain benchmarking, facility management and quality control, and, finally, sensitivity analysis & decision-making.
The construction industry consumes significant resources, emits considerable pollutants, and generates substantial waste. Green, Lean, Six Sigma (GLS) is an emerging paradigm to control waste, carbon footprint, resource conservation, non-value-added activities, and cost. However, limited focus has been given to the risks involved in GLS construction projects (GLSCPs). This research explored risk factors (RFs) to GLSCPs based on literature review and expert judgments. Brainstorming sessions were conducted to validate the RFs and establish mutual interactions among them through experts' opinions. A 4-level structural model was extracted through Interpretive structural modeling (ISM). The Matriced Impacts Croise's Multiplication Appliqée a UN Classement (MICMAC) was integrated to assess the ‘driving’ and ‘dependence’ power of the RFs. The results show that all RFs are crucial and impact GLSCPs, but the most critical are ‘unstable inflation,’ ‘fluctuations in interest rate,’ and ‘fluctuations in exchange rate.’ This study enhances managers' and policymakers' understanding of RFs associated with GLSCPs and supports effective risk management for successful GLS implementation in construction projects.
Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.
Mohammed Mehadi Masud Mazumder, Dewan Mahboob Hossain
PurposeCybersecurity disclosure (CSD) provides users with valuable information and significant insights about a firm's susceptibility to cyber risk and its management. It is argued that the board of directors, with its oversight role, should be vigilant in managing cyber risk and disclosures. This study aims to measure the extent of CSD of the banking companies and examines the association between the characteristics of board composition (i.e. board size, board independence and gender diversity) and CSD.Design/methodology/approachThis study adopted automated content analysis to find out the extent of CSD in the listed commercial banks of an emerging country, Bangladesh, where CSD is voluntary. Further, multiple linear regression is applied to determine the relationship between board composition and CSD.FindingsThe findings reveal an increasing trend of CSD over the sample period (2014–2020). The study confirms a significant positive relationship between board independence and CSD. The study also demonstrates that the higher presence of female directors on the board is associated with higher CSD. However, no consistently significant relationship is found between board size and CSD.Research limitationsThe study is based on listed banking companies only. Hence, the results can not be generalised to companies in other sectors. Also, it is important to acknowledge that we focused on the quantity (not the quality) of CSD contained in annual reports.Practical implicationsThe study provides an overall understanding of current trends of CSD in the Banking sector of a developing country. Regulators may use our findings to understand the current level of CSD and assess the need for issuing guidance in this regard. The association between board composition and CSD has implications both for banks when selecting board members and policymakers when establishing requirements concerning board composition under corporate governance guidelines.Originality/valueThis is one of the very few studies in the context of an emerging economy where CSD is voluntary. The paper contributes to a narrow stream of research investigating CSD and its association with board composition. Notably, it contributes to understanding how board composition is associated with CSD in the banking industry, which is highly exposed to cyber risk.
Sarvesh Patil, Anjana Adhyapak, Priya Shetti
et al.
Abstract Background Analytical quality by design is a proactive, holistic, and data-driven approach to quality that emphasizes risk assessment and management. This can lead to more robust and reliable methods than traditional approaches. Using principles of analytical quality by design for method development can help to assure the quality and consistency of analytical methods. This is important for the pharmaceutical industry, where accurate and reproducible analytical methods are essential for ensuring drug safety, shelf life, and efficacy. Esculin is a naturally occurring derivative of coumarin that is found in the stems of the plant Aesculus indica. The present study describes the use of an analytical quality by design approach to develop and validate a reliable RP-HPLC method for the analysis of esculin bulk form. Result A central composite design was employed to optimize the percent of methanol in the mobile phase and flow rate for the analysis of a compound esculin using the RP-HPLC method. The optimized conditions were 43% methanol and 0.9 ml/min flow rate, with a retention time of 3.78 min, and Phenomenex Luna (5 µm × 250 mm, 4.6 mm) column was used. The method was found to be linear with a correlation coefficient of 0.9998 for a concentration range of 4–20 μg/ml. The parameters of the system suitability test were within the acceptable range (0.0612–0.1398%), and the precision for both intra-day and inter-day measurements was below 2%. The robustness and ruggedness of the method were also good, with changes in the flow rate and mobile phase composition having a minimal impact on the method's performance. The limit of detection (LOD) and limit of quantification (LOQ) values were reported to be 0.82891 μg/ml and 2.511 μg/ml, respectively. The validation parameters of the method adhered to the specified limit following the ICH guidelines. Conclusion In summary, an AQbD-based efficient and robust RP-HPLC chromatographic method has been developed for the quantification of the esculin compound. The method is linear, precise, and reproducible, and it has good LOD and LOQ values. The method could be used for repetitive analysis of the compound in pharmaceutical formulations.
Therapeutics. Pharmacology, Pharmacy and materia medica
Groundwater is an indispensable source of water for drinking water supply, agriculture and industry worldwide. In arid and semi-arid regions, groundwater has seriously deteriorated in recent decades due to environmental changes, anthropogenic activities and marine intrusion. A total of 79 groundwater samples from the Nekor-Ghiss plain were sampled for major chemical ion analysis. These analyzes showed that the water samples were highly mineralized (>1,500 mg/L), with hardness (83.5% of the samples were very hard) and high concentrations of chemical elements, such as Cl−, Mg2+, Na+ and SO42-. To assess the quality of water in the study area for irrigation and consumption purposes, we used the quality index (GWQI) as well as a multi-criteria analysis based on “geographic information system” by assigning a weight to the different water quality parameters. Also, Piper and Durov diagram was investigated. The results of the study were focused on the water quality parameters of the collected groundwater samples, such as the sodium adsorption rate (SAR), the percentage of soluble sodium (Na%), the Residual Sodium Carbonate (RSC) and Permeability Index (PI) Majority of water samples in the study area are suitable to be used for irrigation. Magnesium Risk (MH) and Kelley’s Ratio Kelly ratio. 51.9% unsuitable samples to 59.49% good samples for irrigation purposes. Monitoring the quality and quantity of groundwater is crucial for the effective and sustainable management of this valuable resource. According to the results obtained, it appears that 92% of all the samples are located in the domain of frequent recharge waters of limestone and dolomitic aquifers, namely, Ca-Mg-HCO3. About 8% of the samples measured have a composition of the Ca-Na-Mg-HCO3 type.