The purpose of this paper is to describe and extend the use of the newly-introduced measure, residual estimation risk. Following the seminal work of Bignozzi and Tsanakas, the quantification of residual estimation risk is proposed in a multivariate framework. Our aim is to provide a succinct and practical introduction to the concept, to motivate its use as a back-testing measure, and to provide examples related to credit risk parameter estimation. In section 2, we introduce residual estimation risk defined by various risk measures, and illustrate the calculation using R and SAS. In section 3, we propose a back-testing criterion for the measure, which can be altered to assess model performance for both accuracy and conservatism. In section 4, we conduct back-testing on risk parameter estimates of retail credit portfolios, including multiple back-testing measures for comparison. Finally, we conclude our findings and propose areas for future work in section 5.
Elisa Botteghi, Martino S. Centonze, Davide Pastorello
et al.
Cyber risk has become a critical financial threat in today's interconnected digital economy. This paper introduces a cyber-risk management framework for networked digital systems that combines the strategic behavior of players with contagion dynamics within a security game. We address the problem of optimally allocating cybersecurity resources across a network, focusing on the heterogeneous valuations of nodes by attackers and defenders, some areas may be of high interest to the attacker, while others are prioritized by the defender. We explore how this asymmetry drives attack and defense strategies and shapes the system's overall resilience. We extend a method to determine optimal resource allocation based on simple network metrics weighted by the defender's and attacker's risk profiles. We further propose risk measures based on contagion paths and analyze how propagation dynamics influence optimal defense strategies. Numerical experiments explore risk versus cost efficient frontiers varying network topologies and risk profiles, revealing patterns of resource allocation and cyber deception effects. These findings provide actionable insights for designing resilient digital infrastructures and mitigating systemic cyber risk.
The aim of this article is to forecast the systemic risk contribution and exposure measured by the delta conditional value at risk (ΔCoVaR) and the marginal expected shortfall (MES), respectively. We first estimate the ΔCoVaR and MES for banks in 16 European countries for the 2002–2016 period. We then predict systemic risk measures using machine learning techniques, such as artificial neural network (ANN) and support vector machine (SVM), and we use AR-GARCH specification. Finally, we compare the methods’ forecasting values and the actual values. Our results show that two hidden layers of artificial neural networks perform efficiently in forecasting systemic risk.
Agwu A. Ejem, Somtochukwu V. Okeke, Rachael O. Ojeka-John
et al.
This article reviewed bodies of existing local and international literature to provide multi-level insights into Africa’s readiness to standardise the adoption of social media and associated technologies in managing the numerous climate-related disasters in Africa, including storms, floods and droughts. Social media is making serious inroads in disaster management globally, except in Africa, with countries such as the United States of America, Japan, Haiti, Australia and so on, effectively deploying social media technologies in different cycles of disaster management, particularly since 2010. To encourage disaster management stakeholders in Africa to mainstream the involvement of social media in disaster management, this study examined Africa’s prospects using force-field analysis that assessed the social, financial, policy, technological and other factors that inspire or restrain the effective and comprehensive adoption of social media technologies in disaster management. The force-field analysis demonstrated that disaster management stakeholders in Africa have all the tools and conditions to adopt social media technologies in climate-related disaster management on the continent.
Contribution: Driving forces such as the steady Internet access and penetration in Africa, fast-growing social media penetration and adoption of mobile technology, Africa having four of the top 10 countries that spend the most time on social media globally, growing investments in Internet infrastructure and communalistic nature of African societies, among others, are pointers of Africa’s readiness to mainstream social media technologies in climate change-related disaster management.
The construction industry, a cornerstone of global economic growth, faces frequent safety accidents due to its complex environments and multi-party collaboration, impeding sustainable development. These incidents arise from interlinked causal factors, including human error, management shortcomings, technical failures, and environmental conditions. This study systematically reviews construction accident causation research by integrating scientometric analysis and qualitative methods, using VOSviewer to analyze literature from Scopus and Web of Science databases, with 110 peer-reviewed articles selected through a validated Boolean search strategy. VOSviewer was used for bibliometric visualization to map research trends, co-authorship networks, and keyword co-occurrences. In addition, a qualitative synthesis was conducted to review common data sources and examine key issues, including risk factor identification, accident type classification, causality analysis, and the optimization of research strategies. The study aims to systematically review the current state of construction accident causation research, highlighting key trends in data-driven and AI-based safety interventions. Findings reveal a shift toward data-driven, intelligent approaches, with artificial intelligence techniques—such as large models (capable of understanding complex patterns from massive datasets), graph neural networks (suitable for modeling relationships between contributing factors), and natural language processing (for extracting insights from textual accident reports)—enhancing accident prevention and risk prediction. Challenges persist, however, in data quality, causal exploration depth, and interdisciplinary integration. These findings underscore the need for further advancements in data accuracy and model scalability, which could inform more effective safety management practices and policy frameworks. Key contributions include filling the bibliometric gap in this field, offering a novel framework combining quantitative and qualitative insights, and highlighting advanced technology applications, thus providing theoretical and practical guidance for future safety management. Future research is recommended to leverage AI, foster interdisciplinary collaboration, and develop precise prevention systems to address these gaps.
Engineering (General). Civil engineering (General), City planning
Md. Mizanur Rahaman, Bhavya Sharma, Saranika Talukder
et al.
Viral diseases pose significant threats to aquaculture industries worldwide, including the Australian fish and prawn farming sectors, which contribute over AUD 1.6 billion annually to the national economy. The Australian aquaculture industry relies heavily on wild-caught broodstock for seedstock production, introducing substantial and unprecedented biosecurity risks. This systematic review consolidates current knowledge on the viral pathogens affecting key Australian fish and prawn species, their economic impacts, and the biosecurity measures implemented for mitigation. Notably, viral outbreaks have led to losses exceeding AUD 100 million in some sectors, highlighting the urgent need for improved management. Existing biosecurity strategies, including surveillance systems, molecular diagnostics, and pathogen exclusion protocols, are critically assessed for their effectiveness. Emerging approaches such as genetic resistance breeding, advanced vaccination technologies, and integrated risk management frameworks are also explored. Key knowledge gaps, particularly in the context of emerging viral pathogens and their ecological interactions under changing environmental conditions, are identified as priority areas for future research. This review emphasises the necessity of adopting a multidisciplinary approach to enhance the resilience of Australian aquaculture, advocating for stronger biosecurity frameworks and innovative technologies to mitigate the escalating risks posed by viral diseases.
Ali Farahani, Younos Vakil Alroaia, Farideh Haghshenaskashani
et al.
Abstract
The purpose of this research is to design a model of predictive factors of entrepreneurial opportunities in international companies. The research method is applicable in terms of purpose, and qualitative in terms of its implementation. The statistical population of the research includes 17 managers and experts active in the field of entrepreneurship, and the sampling was done in a purposeful and snowball manner, and the interviews continued until reaching theoretical saturation. The data collection tool is a semi-structured interview. Data-based method was used to collect and analyze data. Data analysis and pattern design were done in three stages of open, central, and selective coding. For data analysis, MAXQDA 18 software was used for coding. In the open coding stage, 305 preliminary codes were identified out of the analysis of the interviews. In the second stage; core coding based on research findings, the concept of entrepreneurial opportunities was chosen as the core phenomenon. Causal conditions were placed in the form of seven categories: unexpected events, changes based on industry and market structure, shortage based on methods, contradictory situation, change based on values and knowledge, new knowledge, and demographic characteristics; and the four main categories were selected include organizational strategies, market related strategies, business strategies, and effective performance management. Intervening factors were identified in two strengthening and weakening categories. Capital, corporate factors, social learning, gathering and studying information, individual characteristics, and social factors were determined as the background factors of entrepreneurial opportunities. Finally, the consequences of predictors of entrepreneurial opportunities were determined in two categories: financial and tangible, and non-financial and intangible.
Extended Abstract
Introduction
According to entrepreneurship researchers, opportunity identification plays a very fundamental role in entrepreneurial activities. Entrepreneurship creates and recreates value for owners and beneficiaries, and opportunity is the heart of this process. Although opportunity recognition is considered the main characteristic of entrepreneurs and entrepreneurial activity does not occur without it, not all people are able to recognize opportunities; thus not all people can achieve entrepreneurial activities (Jaber et al, 2022). Several studies have been conducted regarding the factors affecting opportunity identification to become the basis for increasing the identification of entrepreneurial opportunities. However, due to the importance of identifying opportunities in the entrepreneurial process, there is still a gap in studies in this field (Virasa et al, 2022). Identifying entrepreneurial opportunities is a relatively new issue that is known as an effective and sustainable solution for the economic and social development of countries, and attracts the attention of wider sections of society every day. The importance of identifying entrepreneurial opportunities is due to the fact that finding predictive factors are effective in identifying profitable opportunities that can be implemented in most parts of the world. Our country is also on the path of taking steps towards development and progress, and putting forward a comprehensive research on factors predicting entrepreneurial opportunities in Iran can help accelerate the process of progress. On the other hand, Iran is a country that has a long and fruitful history in the field of entrepreneurial activities, and attention to a new approach in this field leads to the actualization of the potential capabilities in this field (Abdi, 2015). Based on this, the current research is looking for an answer to this question: How is the design of the model of predictive factors of entrepreneurial opportunities in international companies?
Theoretical Framework
Entrepreneurship
Entrepreneurship is a management attitude that gives meaning to concepts such as innovation, flexibility and accountability based on understanding environmental opportunities. Organizational entrepreneurship occurs when an organization relies on the growth and use of new opportunities of internal and external factors of its organization (Davali et al, 2022).
Entrepreneurial opportunities
An entrepreneurial opportunity includes a set of ideas, beliefs, and actions that make it possible for them to create future products and services in the absence of current markets. Identifying an entrepreneurial opportunity is the understanding of the possibility of creating a new business or specifically improving the existing situation of a business in such a way that leads to a new profitability potential. In other words, identifying an entrepreneurial opportunity is the ability to identify a good idea and turn that idea into a business concept that has value and economic returns (Scott & Scott, 2016).
Panahi et al, (2024) investigated the impact of entrepreneurial intention on students' entrepreneurial behavior regarding the moderating role of fear of failure and economic literacy. The results of the research showed that the variables of independence, innovation, and risk-taking of students have a positive and significant effect on entrepreneurial intention. Also, entrepreneurial intention has a positive and significant effect on entrepreneurial behavior. The fear of failure variable has a negative adjustment of the relationship between intention and behavior, and the economic literacy variable has a positive adjustment on this relationship. The research findings show the important role of personality traits on entrepreneurial intention and the importance of moderators introduced to fill the gap between entrepreneurial intention and behavior.
Fries & Jilnek (2023) investigated the psychology of entrepreneurship: action and process in a research. This study reviews entrepreneurial psychology research in the last decade; and focuses on two key themes in entrepreneurship research: action, and process. By combining action and process in a model of entrepreneurial psychology, the process model presents the theory of entrepreneurial action, which is used as a guiding framework for investigation. Theories of action are discussed, such as cause/effect, bricolage, theory of planned behavior, and action theory. In addition, they adopt a process perspective to discuss the antecedents of actions in terms of cognition, motivation, and emotions; and how they develop during the entrepreneurial process. The results of the research showed that the action theory process model is a useful starting point for explaining the psychological dynamics of entrepreneurship.
Research methodology
The research method is applicable in terms of purpose, and qualitative in terms of its implementation. The statistical population of the research includes 17 managers and experts active in the field of entrepreneurship, and the sampling was done in a purposeful and snowball manner, and the interviews continued until reaching theoretical saturation. The data collection tool is a semi-structured interview. Data-based method was used to collect and analyze data.
Research findings
Data analysis and pattern design were done in three stages of open, central, and selective coding. For data analysis, MAXQDA 18 software was used for coding. In the open coding stage, 305 preliminary codes were identified out of the analysis of the interviews. In the second stage; core coding based on research findings, the concept of entrepreneurial opportunities was chosen as the core phenomenon. Causal conditions were placed in the form of seven categories: unexpected events, changes based on industry and market structure, shortage based on methods, contradictory situation, change based on values and knowledge, new knowledge, and demographic characteristics; and the four main categories were selected include organizational strategies, market related strategies, business strategies, and effective performance management. Intervening factors were identified in two strengthening and weakening categories. Capital, corporate factors, social learning, gathering and studying information, individual characteristics, and social factors were determined as the background factors of entrepreneurial opportunities. Finally, the consequences of predictors of entrepreneurial opportunities were determined in two categories: financial and tangible, and non-financial and intangible.
Conclusion
The current research was conducted with the aim of designing a model of predictive factors of entrepreneurial opportunities in international companies. The results of this research are in agreement with the results of Panahi et al, (2024), Fries & Jilnek (2023), Abadeh et al, (2022), BurujAli (2022), Jaber et al, (2022), Doanh (2021), Bazkiaei et al, (2020), Hamzeh Ni Tehrani et al, (2022), Ghanizadeh et al, (2020), Zivodar (2019), Ahmadi et al, (2018), and Vaghely & Julien (2015).
Hamzeh Ni Tehrani et al, (2022) in their research confirmed the factors of personality characteristics and environmental factors as strengthening factors in discovering entrepreneurial opportunities. Effective strategies on the discovery of entrepreneurial opportunities were also, by identifying sixty-five concepts, placed in the form of six sub-categories and four main categories of organizational strategies, market-related strategies, business strategies, and effective performance management.
According to the results of the research, the following suggestions are presented:
- continuous monitoring of changes and trends in society; these changes, which are influenced by social and economic factors and government policies, create new challenges and opportunities for companies that can make the best use of these opportunities with careful planning;
- Creating an open communication channel in the company environment; If there is no open communication in the workplace, people's creativity will be damaged. To bring innovation to the workplace, employees must be informed that the door is always open for discussion
Introduction: Supply chain disruption is an event that disrupts the production of goods and services. Resilience refers to the ability of an organization to manage disruptions or the ability of the supply chain network to quickly return to its previous state, ultimately positively impacting the company's performance. Many companies cannot maintain productivity during disruptions, losing competitiveness, increasing business continuity risk, and incurring financial losses. Sustainability considerations in supply chain operations have become a key issue. A common concept in sustainability is the triple approach: economic, environmental, and social, which must be observed by supply chain members. Sustainable supply chain management development is not a limiting factor but an approach to improve performance.Methods: This applied research study was conducted using a mixed qualitative-quantitative analysis with a cross-sectional survey method. The qualitative sample included academic and industry experts, while the quantitative sample comprised managers, heads, and experts in the studied company's headquarters, operations, and projects. Data collection tools included documentary studies, expert surveys, and a researcher-made questionnaire. Factors were identified using the meta-synthesis technique, screened with the fuzzy Delphi technique, and validated with partial least squares. The SWARA method was used for weighting and ranking factors. Supply chain processes were defined based on the SCOR model and ranked using the WASPAS method. The thinking process tools identified limitations in the third-level bottleneck process, and improvement solutions were presented.Results and Discussion: The meta-synthesis method extracted the desired indicators, which were screened and localized using the fuzzy Delphi technique and confirmed by experts in 7 dimensions and 39 indicators. The initial model was validated with partial least squares. Among resilience and sustainability factors, the "Risk Management" dimension with a weight of 0.2241 and the "Considering the risk factor in decision-making" index with a weight of 0.1224 were the top priorities. It was concluded that risk management is crucial for business continuity and dynamism. Supply chain managers should facilitate their participation in identifying and controlling risks and opportunities while continually increasing their subordinates' knowledge and skills. Evaluations identified the "sourcing and supply process," "goods and logistics supply process," and "purchase planning" as the most critical bottleneck processes. The root of disruptions in the "purchase planning" process was found to be in the identification, estimation, and allocation of human, infrastructural, and financial resources. Conclusions: Practical suggestions for company managers and decision-makers include employing expert personnel in purchasing planning, drafting executive plans, using advanced tools for measurement, analysis, forecasting, resource allocation, identifying uncertainties, determining prerequisites, and managing main and support suppliers and changes, and reviewing and modifying the existing mechanism.
Phindile Madikizela, Janice Limson, Ronen Fogel
et al.
Temporal trend analysis of the Google-search volumes and terms related to water, sanitation and hygiene (WASH) in South Africa was performed using a computer plugin between January 2004 and June 2022. This study was conducted as WASH has played an important role in the containment of the recent coronavirus disease 2019 (COVID-19) pandemic, and it is also one of the most effective and easiest-to-deploy tools in decreasing risk from infectious diseases. For the WASH-related terms, the monthly search volumes ranged from the minimum average of 480 for pit latrines to the maximum of 30236 for diarrhea or diarrhoea for the studied period. The Spearman correlation coefficients ranged from –0.29462 to 0.96647, with the p-values ranging from 0.00001 to 0.28789. On a yearly basis, there was a direct correlation between the WASH-related search volumes extracted and the access of the South African population to basic water and sanitation. There was an inverse relationship between the WASH-related search volumes extracted on an annual basis and the death rates from diarrhoeal diseases among children under 5 years of age in South Africa between 2004 and 2020. Results of the current study indicate that a Google-derived search volume can be useful in the assessment of the public’s interest in WASH-related topics in South Africa.
Contribution: Therefore, the study findings could be used to optimise the design and targeting of public awareness campaigns on WASH during the coronavirus pandemic or similar infectious disease burdens and related disaster risks.
This study examines the transformative impact of the REIT Investment Diversification and Empowerment Act (RIDEA) of 2007 on Real Estate Investment Trusts (REITs) and their partnerships with operating entities in the U.S. senior housing industry. We explore how REITs, functioning as both asset owners and managers, adapt their business strategies in response to the evolving business landscape. Employing a case study approach rooted in Zott and Amit’s (2010) conceptualization of business models, the study identifies and analyzes notable shifts in market participants’ recognition of value-enhancing approaches, encompassing not only traditional rental income but also the operational performance of property managers. The findings reveal an expanded risk and profit-sharing mechanism propelled by the newly implemented business framework based on RIDEA, fostering enhanced alignment of interests between REITs and operators compared to the traditional business framework. While this effect under the new model holds the potential for significant enhancements in operational efficiency for asset managers, it concurrently introduces complexities arising from heightened financial and market risks, as well as challenges related to workforce management. Our findings offer valuable insights to industry experts, including REITs, operators, investors, and policymakers, enriching their comprehension of the evolving business models within the senior housing sector.
This paper investigates the challenge of decarbonizing the steel industry, a pillar of the global economy but also a major carbon emitter. Analyzing current decarbonization strategies, their effectiveness, and the role of corporate commitment and risk management offers insights needed to identify development paths in the current environment characterized by pressure driven by stringent environmental standards and fierce competition. An empirical approach, including a survey model and simulation, is used to answer prominent research questions. Aspects such as the influence of environmental and governance criteria, specific initiatives that can be undertaken, the importance of corporate commitment, and the integration of risk management into strategic planning are examined. Simulations suggest that the probability of meeting the 2030 goals range from 65.08 to 75.98 percent and the delta between low and high commitment ranges from 4.917 to 4.133 percent according to the share of renewables in the energy mix decarbonization. The influence of the energy mix is also included in the analysis. The research highlights the need for greater coordination and commitment across the industry to improve decarbonization efforts. It emphasizes the critical role of government policies and market dynamics in shaping industry actions toward achieving decarbonization goals. The findings contribute to understanding decarbonization processes, offering insights and guidance for the steel sector's transition to a low-carbon economy.
Víctor González, Javier Godoy, Félix Meléndez
et al.
Monitoring chemical substances is of paramount importance in various industries and environmental contexts. In industrial settings, the presence of certain chemical substances may indicate leaks, spills, or malfunctioning equipment, posing immediate threats to both human health and the environment. Moreover, continuous monitoring of chemical compounds is crucial for ensuring compliance with safety regulations and environmental standards. In the context of air quality management, monitoring chemical compounds helps identify sources of pollution, assess the impact on public health, and implement effective pollution control measures. Timely detection and response to chemical compounds also play a vital role in preventing long-term environmental degradation. Overall, monitoring chemical substances is an indispensable component of proactive risk management, environmental stewardship, and the safeguarding of human well-being. The development of automatic and portable devices for online monitoring is an important need in the chemical industry. The smartwatch designed and presented is a home-developed prototype and built from commercially available components. All components of the smartwatch are protected with a plastic casing capable of allowing air to pass to the sensors, the device is also capable of measuring temperature and relative humidity, magnitudes that influence the detection of different odors or volatile compounds. This device is based on a microcontroller that offers low-power performance, integrated Bluetooth low energy at an affordable price, and the measurements of four digital MOX gas sensors, models BME680 SGP40, ENS160 and STC31 through I2C interface. Data are shown on a LCD display and also transmitted via Bluetooth to a smartphone at a sampling period time of 2 s. It is powered using a 3.7 V lithium polymer battery. The smartwatch has a graphical interface to show the user the data provided by the sensors. The designed smartwatch has been validated by measuring different industrial gases like toluene, xylene, and ethylbenzene at low concentrations. Toluene was measured at 6 ppm, xylene at 8 ppm, and ethylbenzene at 10 ppm. Good discrimination between the three different gases was achieved using Principal Component Analysis as multivariable analysis.
Chemical engineering, Computer engineering. Computer hardware
In this paper, we introduce a new class of set-valued risk measures, named set-valued star-shaped risk measures. Motivated by the results of scalar monetary and star-shaped risk measures, this paper investigates the representation theorems in the set-valued framework. It is demonstrated that set-valued risk measures can be represented as the union of a family of set-valued convex risk measures, and set-valued normalized star-shaped risk measures can be represented as the union of a family of set-valued normalized convex risk measures. The link between set-valued risk measures and set-valued star-shaped risk measures is also established.
Alessandra Amendola, Vincenzo Candila, Antonio Naimoli
et al.
To comply with increasingly stringent international standards in risk management and regulation, several approaches have been developed in the literature for forecasting tail-risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). However, the accuracy of these measures can be significantly affected by multiple sources of uncertainty, including model misspecification, data limitations and estimation procedures. To address these challenges and enhance the predictive performance of individual models, this study introduces novel forecast combination strategies based on the Model Confidence Set (MCS) methodology. Specifically, a strictly consistent joint VaR-ES loss function is employed to identify the best-performing models, which constitute the Set of Superior Models (SSM). Subsequently, the VaR and ES forecasts of the models included in the SSM are combined using various weighting schemes. An empirical analysis based on nine stock market indices at the 2.5\% and 1\% risk levels provides evidence that the proposed combined predictors are a robust alternative for forecasting tail-risk measures, successfully passing standard backtests and consistently entering the SSM of the MCS.
Credit risk stress testing has become an important risk management device which is used both by banks internally and by regulators. Stress testing is complex because it essentially means projecting a bank's full balance sheet conditional on a macroeconomic scenario over multiple years. Part of the complexity stems from using a wide range of model parameters for, e.g., rating transition, write-off rules, prepayment, or origination of new loans. A typical parameterization of a credit risk stress test model specifies parameters linked to an average economic, the through-the-cycle, state. These parameters are transformed to a stressed state by utilizing a macroeconomic model. It will be shown that the model parameterization implies a unique through-the-cycle portfolio which is unrelated to a bank's current portfolio. Independent of the stress imposed to the model, the current portfolio will have a tendency to propagate towards the through-the-cycle portfolio. This could create unwanted spurious effects on projected portfolio default rates especially when a stress test model's parameterization is inconsistent with a bank's current portfolio.
The basic principle of any version of insurance is the paradigm that exchanging risk by sharing it in a pool is beneficial for the participants. In case of independent risks with a finite mean this is the case for risk averse decision makers. The situation may be very different in case of infinite mean models. In that case it is known that risk sharing may have a negative effect, which is sometimes called the nondiversification trap. This phenomenon is well known for infinite mean stable distributions. In a series of recent papers similar results for infinite mean Pareto and Fréchet distributions have been obtained. We further investigate this property by showing that many of these results can be obtained as special cases of a simple result demonstrating that this holds for any distribution that is more skewed than a Cauchy distribution. We also relate this to the situation of deadly catastrophic risks, where we assume a positive probability for an infinite value. That case gives a very simple intuition why this phenomenon can occur for such catastrophic risks. We also mention several open problems and conjectures in this context.
Informally, a risk measure is said to be elicitable if there exists a suitable scoring function such that minimizing its expected value recovers the risk measure. In this paper, we analyze the elicitability properties of the class of return risk measures (i.e., normalized, monotone and positively homogeneous risk measures). First, we provide dual representation results for convex and geometrically convex return risk measures. Next, we establish new axiomatic characterizations of Orlicz premia (i.e., Luxemburg norms). More specifically, we prove, under different sets of conditions, that Orlicz premia naturally arise as the only elicitable return risk measures. Finally, we provide a general family of strictly consistent scoring functions for Orlicz premia, a myriad of specific examples and a mixture representation suitable for constructing Murphy diagrams.
Suchi Rahmadani, Irwan Meilano, Susilo Susilo
et al.
The Banda Arc region has produced several large destructive earthquakes, some of which have been followed by tsunamis. To better understand the earthquake potential in this area, we performed a comparison between geodetic and seismic moment rates. Data were collected from 110 continuous and campaign GPS stations observed for approximately ten years. The results show that the derived velocity field indicates that the Banda Arc deformation is characterized mainly by crustal shortening caused by the interaction of the Australian, Pacific, and Philippine Sea plates. Meanwhile, the contraction strain pattern dominates the Banda Arc area except around Papuan Bird’s Head. Areas with high strain rates have a history of significant seismicity, such as the Flores-Wetar Back Arc, the area around Ambon, and the Papuan Bird’s Head. The ratio of the geodetic moment rate to the seismic moment rate in the Banda, Bird’s Head, South Sulawesi, and Sumba zones are ∼1.5–7.0, indicating a moment deficit rate. The moment deficit rate provides an equivalent earthquake potential of Mw 7.7–8.1. This potential may be related to an aseismic deformation or stress accumulation, the under-sampling of long-term earthquake rates within the seismic catalogs, or a composite of these factors.
In recent years, after the economic crises, the value of operational risk assessment has been observed in the financial industry, while the biggest impact of operational risk has been on the banking industry. As a result, more attention has been paid to operational risk assessment in the banking industry after the financial crisis. Operational risk is defined as the risk of loss caused by the inadequacy or inefficiency of internal processes, people, systems, or external events. In this regard, institutions and banks have been looking for operational risk assessment by taking different approaches, including the approaches of the Basel Committee. The current research aimed to review operational risk management in the banking industry. Using the meta-synthesis method, 643 related research documents between 2000 and 2022 were gathered from among reliable scientific databases. By using this method, 43 final documents made the basis for extracting the findings. Finally, this method identified 5 main categories, including operational risk, risk assessment, risk quantification methods, risk analysis, and risk management, as well as 10 subcategories consisting of 43 concepts and 169 codes. The results were confirmed based on the experts’ opinions with a kappa index of 0.756.Keywords: Operational risk, Advanced Measurement Approach, Basic Index Approach, Standard Approach, Risk Management. IntroductionThe significant losses suffered by financial and non-financial institutions from various non-credit and non-market processes and factors have made many managers and decision-makers of these organizations pay attention to the field of "operational risk". Operational risk and its management methods are significant topics in the banking industry. They have potential effects on the performance of banks and financial institutions. According to the conducted research, authors have looked at risk from different perspectives. The relevant groups have focused on the definition of risk, classification of operational risk events, measurement and characteristics of operational risk management, and comparative analysis of different estimates (Barakat & Hussainey, 2013). Method and DataThe current study was an applied research based on collecting documentary information. Using the meta-combination method, 643 related research documents between 2000 and 2022 were gathered from reliable scientific databases. By using this method, 43 documents finally made the basis for extracting the findings. FindingsUsing the meta-synthesis method, 643 related research documents between 2000 and 2022 were gathered from reliable scientific databases. By using this method, 43 documents made the basis for extracting the findings. Finally, this method identified 5 main categories, including operational risk, risk assessment, risk quantification methods, risk analysis, and risk management, as well as 10 subcategories, 43 concepts, and 169 codes. The results were confirmed based on the experts’ opinions with a kappa index of 0.756. Discussion of results & Conclusion In this research, the studies conducted in the field of operational risk were classified by using the meta-synthesis method. The meta-synthesis method was consisted of 7 steps. First, the questions related to the research were designed. In the second stage of the systematic literature review, 643 related articles in the field of operational risk from 2000 to 2022 were collected. In the next step, the related articles were selected based on title, abstract, and text. Then, 43 related articles were identified and 169 codes were extracted. Afterwards, the data analysis and data synthesis were done and the numbers of main categories, subcategories, and concepts were identified. In the sixth stage, quality control was performed and used to determine the value of Cronbach's alpha from the experts’ points of view. In the last stage, the relationships between the research findings were shown by using a tree diagram. In the field of operational risk, the meta-synthesis method had not been used for systematic review, classification, and categorization of the results of the articles. In this research, unlike the previous research, the related articles were classified by using the meta-synthesis method. Then, the categories and concepts were extracted and the relationships between them were shown in the form of a tree diagram.
Probabilistic risk aversion, defined through quasi-convexity in probabilistic mixtures, is a common useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions, which includes the preference models of expected utilities, dual utilities, and rank-dependent utilities as special cases, as well as signed Choquet functions used in risk management. Our results fully characterize probabilistic risk aversion for generalized rank-dependent functions: This property is determined by the distortion function, which is precisely one of the two cases: those that are convex and those that correspond to scaled quantile-spread mixtures. Our result also leads to seven equivalent conditions for quasi-convexity in probabilistic mixtures of dual utilities and signed Choquet functions. As a consequence, although probabilistic risk aversion is quite different from the classic notion of strong risk aversion for generalized rank-dependent functions, these two notions coincide for dual utilities under an additional continuity assumption.