Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There (...) are two types of credit risk default models in this regard: structural and reduced-form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

This article presents a comprehensive framework for valuing financial instruments subject to credit risk. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats in financial markets. We analyze how swap rates are affected by bilateral counterparty credit risk, and how CDS spreads depend on the trilateral credit risk of the buyer, seller, and reference entity in a contract. Moreover, (...) we study the effect of collateralization on valuation, since the majority of OTC derivatives are collateralized. The model shows that a fully collateralized swap is risk-free, whereas a fully collateralized CDS is not equivalent to a risk-free one. (shrink)

This paper argues that the reduced-form jump diffusion model may not be appropriate for credit risk modeling. To correctly value hybrid defaultable financial instruments, e.g., convertible bonds, we present a new framework that relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage (...) is mainly due to convertible underpricing. Empirically, however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate (...) counterparty risk completely in the CDS market. (shrink)

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could (...) be positive or negative. Moreover, the model can naturally capture wrong or right way risk. (shrink)

Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or loss (...) of business reputation influences the financial results and the degree of customers’, partners’ and stakeholders’ confidence. By means of imitating modeling based on Bayesian Networks and the fuzzy data analysis, the article characterizes the mechanism of reputation risk assessment and possible losses evaluation in banks by plotting normal and lognormal distribution functions. Monte-Carlo simulation is used to calculate the probability of losses caused by reputation risks. The degree of standardized histogram similarity is determined on the basis of the fuzzy data analysis applying Hamming distance method. The tree-like hierarchy based on the OWA-operator is used to aggregate the data with Fishburne's coefficients as the convolution scales. The mechanism takes into account the impact of criteria, such as return on equity, goodwill value, the risk assets ratio, the share of the productive assets in net assets, the efficiency ratio of interest bearing liabilities, the risk ratio of credit operations, the funding ratio and reliability index on the business reputation of the bank. The suggested methods and recommendations might be applied to develop the decision-making mechanism targeted at the implementation of reputation risk management system in commercial banks as well as to optimize risk management technologies. (shrink)

The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, (...) and data privacy to determine the benefits and potential risks associated with generative AI in these fields. (shrink)

In this note, we consider a Merton model for default risk, where the firm’s value is driven by a Brownian motion and a compound Poisson process.

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There (...) are two types of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

This article presents a new model for valuing financial contracts subject to credit risk and collateralization. Examples include the valuation of a credit default swap (CDS) contract that is affected by the trilateral credit risk of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully (...) collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market. (shrink)

Economic models describe individuals in terms of underlying characteristics, such as taste for some good, sympathy level for another player, time discount rate, risk attitude, and so on. In real life, such characteristics change through experiences: taste for Mozart changes through listening to it, sympathy for another player through observing his moves, and so on. Models typically ignore change, not just for simplicity but also because it is unclear how to incorporate change. I introduce a general axiomatic framework for (...) defining, analysing and comparing rival models of change. I show that seemingly basic postulates on modelling change together have strong implications, like irrelevance of the order in which someone has his experiences and ‘linearity’ of change. This is a step towards placing the modelling of change on solid axiomatic grounds and enabling non-arbitrary incorporation of change into economic models. (shrink)

This paper presents a new model for valuing hybrid defaultable financial instruments, such as, convertible bonds. In contrast to previous studies, the model relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically, however, we do not find evidence (...) supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all (...) reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

In this chapter, one considers finance at its very foundations, namely, at the place where assumptions are being made about the ways to measure the two key ingredients of finance: risk and return. It is well known that returns for a large class of assets display a number of stylized facts that cannot be squared with the traditional views of 1960s financial economics (normality and continuity assumptions, i.e. Brownian representation of market dynamics). Despite the empirical counterevidence, normality and continuity (...) assumptions were part and parcel of financial theory and practice, embedded in all financial practices and beliefs. Our aim is to build on this puzzle for extracting some clues revealing the use of one research strategy in academic community, model tinkering defined as a particular research habit. We choose to focus on one specific moment of the scientific controversies in academic finance: the ‘leptokurtic crisis’ opened by Mandelbrot in 1962. The profoundness of the crisis came from the angle of the Mandelbrot’s attack: not only he emphasized an empirical inadequacy of the Brownian representation, but also he argued for an inadequate grounding of this representation. We give some insights in this crisis and display the model tinkering strategies of the financial academic community in the 1970s and the 1980s. (shrink)

This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a risk-free value. (...) Credit value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the (...) F1-score 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here (...) I argue that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

Risk propagation encompasses a plethora of techniques for analyzing how risk “spreads” in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the lit- erature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated (...) is fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpre- tation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios. (shrink)

Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis (...) and redesign have been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk. The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain. (shrink)

This chapter argues for deregulation of the credit-rating market. Credit-rating agencies are supposed to contribute to the informational needs of investors trading bonds. They provide ratings of debt issued by corporations and governments, as well as of structured debt instruments (e.g. mortgage-backed securities). As many academics, regulators, and commentators have pointed out, the ratings of structured instruments turned out to be highly inaccurate, and, as a result, they have argued for tighter regulation of the industry. This chapter shows, (...) however, that the role of credit-rating agencies in achieving justice in finance is not as great as these commentators believe. It therefore argues instead for deregulation. Since the 1930s, lawgivers have unjustifiably elevated the rating agencies into official, legally binding sources of information concerning credit risk, thereby unjustifiably causing many institutional investors to outsource their epistemic responsibilities, that is, their responsibility to investigate credit risk themselves. (shrink)

Vietnam’s forests are at risk - carbon offset schemes could be the best chance of saving them, say Dr. Quan-Hoang Vuong and Minh-Hoang Nguyen. The value of forests is deeply ingrained in Vietnamese culture. Rừng vàng, biển bạc” [“forests of gold and seas of silver”] is both a metaphor for Vietnam, and a description of its natural wealth. The phrase is everywhere, from political speeches to daily conversation, as is Nhất phá sơn lâm, nhì đâm hà bá [“the worst (...) crime is to destroy the mountains and forests, the second worst is to kill the river”]. Deforestation is a Vietnamese taboo. (shrink)

The article examines the process of formation inventory of the enterprise and determines the optimal volume of commodity resources for sale. A generalization of author’s approaches to the formation and evaluation of inventories of the enterprise is carried out. The marketing-logistic approach was applied for the purpose of distribution groups of commodity resources due to the risk of non-fulfillment the order for the supply of goods of the enterprise. In order to ensure an effective process of commodity provision of (...) the enterprises, the costs associated with the formation of inventories are determined. The formalized scheme of the formation commodity provision and the process of optimization inventory of the enterprise is offered. The analysis the structure of the company’s inventory is carried out, the volume of goods turnover is defined, the stocks are grouped for various clustering characteristics. To conduct the study, statistical information was used on commodity resources of the enterprises, statistical methods (grouping, structure analysis, estimation of dynamic series), tools for assessing the efficiency of inventory use, HML-FMR clustering were used. The necessity of using XYZ and ABC analysis is indicated in order to obtain more reliable results and forecast values of the product support of the enterprise. Economic-mathematical modeling is applied and graphically shown the difference in the formation of commodity resources by various features of HML-FMR clustering. The calculations allow the enterprise to determine the optimal amount of commodity resources in accordance with the needs of consumers and their solvent demand, to plan financial resources for the formation of inventories, to develop assortment policy in accordance with demand for products and their implementation. The results of calculations the volume of merchandising of the enterprise, taking into account the HML-FMR clustering affect the formation of final financial performance of the enterprise — income and profits. (shrink)

Enterprise Risk Management and security have become a fundamental part of Enterprise Architecture, so several frameworks and modeling languages have been designed to support the activities associated with these areas. Archi- Mate’s Risk and Security Overlay is one of such proposals, endorsed by The Open Group. We investigate the capabilities of the proposed security-related con- structs in ArchiMate with regard to the necessities of enterprise security modeling. Our analysis relies on a well-founded reference ontology of security (...) to uncover ambiguity, missing modeling elements, and other deficiencies of the security mod- eling capabilities in ArchiMate. Based on this ontologically-founded analysis, we propose a redesign of security aspects of ArchiMate to overcome its original limitations. (shrink)

This article describes the peculiarities of the formation and modeling of the enterprise risk management system in modern conditions. The main purpose of the research is further development of theoretical and methodical principles and development of practical recommendations aimed at improvement of management of business risks of wholesale enterprises. The critical analysis of literature sources and approaches to solving problems of enterprise risk management testifies to the lack of attention of scientists of the present-day to the investigated (...) problem in the sphere of activity of enterprises of wholesale trade. The urgency of this problem is caused by the existing losses of wholesale companies due to imperfect management as a result of the influence of various business risks. The methodological basis of the research is the system of methods used to obtain the final results: theoretical generalization, scientific abstraction and comparison – to deepen the essence of the concept of "business risk"; analysis and synthesis, grouping – for classification of business risks in the sphere of wholesale trade; methods of analysis, synthesis and logical generalization – to obtain conclusions during determination of the state and problems of realization of business activity in the sphere of wholesale trade of enterprises of Ukraine; systematic approach – for improvement of system of management of business risks of enterprises of wholesale trade on the basis of commercial values. The object of the research is the business risk in the wholesale trade environment. In the article the further development of the risk-based apparatus has been made, the negative consequences of the impact of business risks on the activities of the wholesale trade enterprises have been clarified, the enterprise risk management system of the enterprises has been built, which will reflect an integrated approach to its realization, practical recommendations on the formation of a model of management of business risks of the wholesale trade enterprises on the basis of commercial values. The results of the survey can be useful for business structures, which are focused on improving the management of business risks. (shrink)

This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data (...) is also taken into account, for example data about space weather effects, by drawing on the Space Domain Ontologies, a large system of ontologies designed to support all aspects of space situational awareness. The framework is coupled with a mathematical programming scheme that frames a mathematically optimal approach for decision support, providing a quantitative basis for ranking potential for collision across multiple satellite pairs. The goal is to provide the broadest possible information foundation for critical assessments of collision likelihood. (shrink)

In Risk Management, security issues arise from complex relations among objects and agents, their capabilities and vulnerabilities, the events they are involved in, and the value and risk they ensue to the stakeholders at hand. Further, there are patterns involving these relations that crosscut many domains, ranging from information security to public safety. Understanding and forming a shared conceptualization and vocabulary about these notions and their relations is fundamental for modeling the corresponding scenarios, so that proper security (...) countermeasures can be devised. Ontologies are instruments developed to address these conceptual clarification and terminological systematization issues. Over the years, several ontologies have been proposed in Risk Management and Security Engineering. However, as shown in recent literature, they fall short in many respects, including generality and expressivity - the latter impacting on their interoperability with related models. We propose a Reference Ontology for Security Engineering (ROSE) from a Risk Treatment perspective. Our proposal leverages on two existing Reference Ontologies: the Common Ontology of Value and Risk and a Reference Ontology of Prevention, both of which are grounded on the Unified Foundational Ontology (UFO). ROSE is employed for modeling and analysing some cases, in particular providing clarification to the semantically overloaded notion of Security Mechanism. (shrink)

The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. (...) The plant safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA-safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and rural areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is predicted for multiple factors distribution schemes of multi-criteria analysis. The input–output analysis is explored from the spillover effect, and we conducted Monte Carlo simulations for sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the composite index for biosphere with collective structure of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in an artistic/architectural perspective. The hypothesis is deployed to unify analytic and analogical spatial structure in development urban environments using optimization loads as an example of integrated industrial structure where the process is based on engineering topology of systems ecology. (shrink)

The paper explores the influence of greenwash on green trust and discusses the mediation roles of green consumer confusion and green perceived risk. The research object of this study focuses on Taiwanese consumers who have the purchase experience of information and electronics products in Taiwan. This research employs an empirical study by means of the structural equation modeling. The results show that greenwash is negatively related to green trust. Therefore, this study suggests that companies must reduce their greenwash (...) behaviors to enhance their consumers’ green trust. In addition, this study finds out that green consumer confusion and green perceived risk mediate the negative relationship between greenwash and green trust. The results also demonstrate that greenwash is positively associated with green consumer confusion and green perceived risk which would negatively affect green trust. It means that greenwash does not only negatively affect green trust directly but also negatively influence it via green consumer confusion and green perceived risk indirectly. Hence, if companies would like to reduce the negative relationship between greenwash and green trust, they need to decrease their consumers’ green consumer confusion and green perceived risk. (shrink)

This paper presents an analytical model for valuing interest rate swaps, subject to bilateral counterparty credit risk. The counterparty defaults are modeled by the reduced-form model as the first jump of a time-inhomogeneous Poisson process. All quantities modeled are market-observable. The closed-form solution gives us a better understanding of the impact of the credit asymmetry on swap value, credit value adjustment, swap rate and swap spread.

This article discusses the management of cycli- cal dynamics of the credit sphere. Considering the fact that conventional methods of monetary policy proved to be vulnerable to the process of accumula- tion and realization of risk in the credit sector, we aim to study heterodox methods of credit market regula- tion – administrative (direct) methods of control over fluctuations on the credit market. In particular, based on the analysis of credit limits used in the (...) Socialist Republic of Vietnam, we come to conclusion about comparative effectiveness of this method in curbing excessive amplitude of the credit cycle. However, the price of using this method may be connected with re- duction in the rate of economic growth and a shift in the accumulation of risk to other areas of the credit market. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously (...) considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Lockdowns, or modern quarantines, involve the use of novel restrictive non-pharmaceutical interventions (NPIs) to suppress the transmission of COVID-19. In this paper, I aim to critically analyze the emerging history and philosophy of lockdowns, with an emphasis on the communication of health evidence and risk for informing policy decisions. I draw a distinction between evidence-based and modeling-based decision-making. I argue that using the normative framework of evidence-based medicine would have recommended against the use of lockdowns. I first review (...) the World Health Organization’s evidence-based pandemic preparedness plans for respiratory viruses. I then provide a very brief history of COVID-19 modeling, which was cited as justification for the use of lockdowns in the U.K., the U.S., and much of the world. I focus on the so-called Imperial College model designed by Neil Ferguson et al. as well as the so-called Oxford model designed by José Lourenço et al. I analyze the evidence-based pandemic response known as ‘mitigation’, and I compare it with Ferguson et al.’s experimental strategy known as ‘suppression’. I summarize the strengths and weaknesses of these strategies based on their diametric aims and each model’s parametric assumptions. Based on my critical analysis of the suppression strategy, I attempt to expose what has been called the ‘logic of lockdowns’, which Sunetra Gupta of the Oxford model group has suggested is flawed. Finally, I consider Trisha Greenhalgh’s objection to evidence-based policy based on the precautionary principle, and I attempt to offer a response. I conclude with a brief narrative review of the emerging randomized evidence on restrictive NPIs, which seems to support my claim that mitigation was the strategy that would have been recommended by evidence-based medicine. If this is true, then COVID-19 modeling may serve as an important reminder of the enduring lesson of evidence-based medicine: that one should always ‘Trust the Evidence!’ for better health policy. (shrink)

The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the (...) dataset collected from Arab countries to study an individual’s mental health and indicative help-seeking pointers based on their responses to specific pre-defined questions in a multicultural society. The proposed model validates the claims mathematically and uses different machine learning classifiers to identify individuals who are either currently or previously diagnosed with depression or demonstrate unintentional “save our souls” (SOS) behaviors for an early prediction to prevent risks of danger in life going forward. The accuracy is measured by comparing with the classifiers using several visualization tools. This analysis provides the claims and authentic sources for further research in the multicultural public medical sector and decision-making rules by the government. (shrink)

This paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently (...) explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. (shrink)

The article investigates the main characteristics of the financial stability of Ukrainian banks, their risk profile, structure of assets, liabilities, income, expenses based on the monthly reporting data for 2004-2020 (number of observations – 3 813). The Kohonen self-organizing map (SOM) toolkit is used to form homogeneous groups of banks based on a large number of financial indicators. The selected toolkit provides a convenient visualization of the results. Each bank occupies a specific place on the SOM. The close location (...) of banks on the map indicates that these banks have many common characteristics. This study is called the method of structural and functional groups of banks. The study formed stable groups of banks with the same characteristics of business models and risk profile. The formation of consecutive maps for each reporting period allows to obtain the characteristics of each bank separately and the banking system as a whole. The trajectory of an individual bank’s activity during the observation period characterizes the development of its business strategy. The migration of a large number of banks between groups reflects the objective changes in the conditions of banking. Increasing groups of high-risk banks indicate systemic problems. The results show that after a significant reduction in the banking system of Ukraine in 2014-2016, the business models of banks have changed significantly. The resource base relies on unstable sources of current funds with no set term of use. In the assets of banks, a significant portion is invested in government securities – government bonds. Bank loan portfolios have declined and have a higher proportion of non-performing assets (NPL). Many banks have increased currency risk. Concerns are raised about the state of large state-owned banks that hold a separate high-risk group. Thus, the conducted research allows to distinguish groups of banks with specific characteristics of business models and to observe changes of risk profile inherent to these groups. For each structural-functional group, changes in the values of financial indicators are investigated and migration between groups is studied. The proposed methodology is useful for studying both macro and macro levels. (shrink)

Bartha and DesRoches (2021) and Steel and Bartha (2023) argue that we should understand the precautionary principle as the injunction to maximise lexical utilities. They show that the lexical utility model has important pragmatic advantages. Moreover, the model has the theoretical advantage of satisfying all axioms of expected utility theory except continuity. In this paper I raise a trilemma for any attempt at modelling the precautionary principle with lexical utilities: it permits choice cycles or leads to paralysis or implies that (...) the smallest value difference that is possible in a context has extreme axiological implications. (shrink)

Climate change mitigation has become a paradigm case both for externalities in general and for the game-theoretic model of the Tragedy of the Commons (ToC) in particular. This situation is worrying, as we have reasons to suspect that some models in the social sciences are apt to be performative to the extent that they can become self-fulfilling prophecies. Framing climate change mitigation as a hardly solvable coordination problem may force us into a worse situation, by changing real-world behaviour to fit (...) our model, rather than the other way around. But while this problem of the performativity of the ToC has been noted in a recent paper in this journal by Matthew Kopec, his proposed strategies for dealing with their self-fulfilling nature fall short of providing an adequate solution. Instead of relying on the idea that modelling assumptions are always strictly speaking false, this paper shows that the problem may be better framed as a problem of underdetermination between competing explanations. Our goal here is to provide a framework for choosing between this set of competing models that allows us to avoid a ‘Russian Roulette’-like situation in which we gamble with existential risk. (shrink)

Artificial Intelligence is becoming widespread and as we continue ask ‘can we implement this’ we neglect to ask ‘should we implement this’. There are various frameworks and conceptual journeys one should take to ensure a robust AI product; context is one of the vital parts of this. AI is now expected to make decisions, from deciding who gets a credit card to cancer diagnosis. These decisions affect most, if not all, of society. As developers if we do not understand (...) or use fundamental modelling principles then we can cause real harm to society. Recently more serious effects of AI have been observed. Dehumanisation is the human reaction to overused anthropomorphism and lack of social contact caused by excessive interaction with, or addiction to, technology. This can cause humans to devalue technology and to devalue other humans. This is a contradiction of the use of ‘social robots’ and ‘chatbots’, indicating that the negative effects of this technology would outweigh any perceived positive effects. Also, within cyberspace, anthropomorphism and similar techniques based on deep philosophical principles can, and are, being used to alter the behaviour of humans. These techniques are used to manipulate human behaviours at a basic level in the human mind. As these types of techniques are becoming more widespread, it is clear that we are entering unchartered territory that holds a vast array of consequences for society. (shrink)

This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a (...) clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets. (shrink)

The authors of the scientific monograph have come to the conclusion that мanagement of socio-economic transformations of business processes requires the use of mechanisms to support of entrepreneurship, sectors of the national economy, the financial system, and critical infrastructure. Basic research focuses on assessment the state of social service provision, analysing economic security, implementing innovation and introducing digital technologies. The research results have been implemented in the different models of costing, credit risk and capital management, tax control, use (...) of artificial intelligence and blockchain. The results of the study can be used in the developing of policies, programmes and strategies for economic security, development of the agricultural sector, transformation of industrial policy, implementation of employment policy in decision-making at the level of ministries and agencies that regulate the management of socio-economic and European integration processes. The results can also be used by students and young scientists in the educational process and conducting scientific research on global challenges and creation scenarios for the development of socio-economic processes. (shrink)

The authors of the book have come to the conclusion that it is necessary to effectively use modern approaches the management of innovative development the economic entities in order to increase the efficiency of activity, to ensure competitiveness, to intensify innovation activity. Basic research focuses on assessing the competition of economic entities, internal control in organizations, analysis of credit risk, diagnostics of sources of funding for innovation, assessment of social innovation and human development factors. The research results have (...) been implemented in the different models of reengineering business process, development of alternative agriculture, the digital economy, knowledge management. The results of the study can be used in decision-making at the level the economic entities in different areas of activity and organizational-legal forms of ownership, ministries and departments that promote of development the economic entities on an innovative basis. The results can also be used by students and young scientists in modern concepts and mechanisms for management of innovative development the economic entities in the context of efficient use the resource potential and improvement of innovation policy. (shrink)

The article provides a critical assessment of The Central Bank of the Russian Federation policy in response to the sanctions of the US, the EU, the UK, Switzerland, Japan, South Korea and a number of other countries. The effect of sanctions on the Russian economy and its financial market is viewed through the prism of credit, interest rate, and currency risk, and the risk of a decline in business activity. Special attention is paid to the inflationary component (...) and inflationary expectations of the Russian Federation, as well as to the forecasts for a decline in business activity in Russia. A critical assessment is given to the actions of the Central Bank of the Russian Federation and the economic bloc of the government of the Russian Federation as a whole in response to the sanctions of the civilized world, which disable the normal existence of the economy and the main purpose of which is not to destroy the economy of the Russian Federation but to ensure the end of hostilities on the European continent. The results of our study will be useful to everyone who studies the problems of the effect of economic sanctions on the resource-based economy and the processes of stimulating political decisions by economic methods. (shrink)

Climate engineering with stratospheric sulfate aerosol injections (SSAI) has the potential to reduce risks of injustice related to anthropogenic emissions of greenhouse gases. Relying on evidence from modeling studies, this paper makes the case that SSAI could have the potential to reduce many of the key physical risks of climate change identified by the Intergovernmental Panel on Climate Change. Such risks carry potential injustice because they are often imposed on low-emitters who do not benefit from climate change. Because SSAI (...) has the potential to reduce those risks, it thereby has the potential to reduce the injustice associated with anthropogenic emissions. While acknowledging important caveats, including uncertainty in modeling studies and the potential for SSAI to carry its own risks of injustice, the paper argues that there is a strong case for continued research into SSAI, especially if attention is paid to how it might be used to reduce emissions-driven injustice. (shrink)

I argue that epistemic failings are a significant and underappreciated moral hazard in the financial services industry. I argue further that an analysis of these epistemic failings and their means of redress is best developed by identifying policies and procedures that are likely to facilitate good judgment. These policies and procedures are “best epistemic practices.” I explain how best epistemic practices support good reasoning, thereby facilitating accurate judgments about risk and reward. Failures to promote and adhere to best epistemic (...) practices contributed to the 2008 financial crisis. I identify and discuss some of the ways in which best epistemic practices were violated in the events that led to the crisis, with a focus on the role of the credit rating agencies. I go on to discuss some of the ways in which these failings have been redressed. I conclude by observing how proactive regulation for best epistemic practices might help us to anticipate and avoid future crises. (shrink)

Clearing algorithms are at the core of modern payment systems, facilitating the settling of multilateral credit messages with (near) minimum transfers of currency. Traditional clearing procedures use batch processing based on MILP - mixed-integer linear programming algorithms. The MILP approach demands intensive computational resources; moreover, it is also vulnerable to operational risks generated by possible defaults during the inter-batch period. This paper presents TORC3 - the Token-Ring Clearing Algorithm for Currency Circulation. In contrast to the MILP approach, TORC3 is (...) a real time heuristic procedure, demanding modest computational resources, and able to completely shield the clearing operation against the participating agents’ risk of default. (shrink)

Nowadays a lot of new financial instruments for SMEs innovation projects are getting more popular in the business environment. A great number of them are traditional like credit loans at the same time we can observe the appearance of innovative ones. Variable set of financial instruments generalized on fig.1. This classification is giving by Organiztion of Economic Cooperation and Development ( OECD). As we can see from the fig.1 the classification is based on risk approach. For further research (...) it is needed to сlarify these definitions. Firstly, asset based finance is the method of assigning structured turnaround capital and term loans, disbursement of debit portfolio, stocks, machines, funds, and / or real estate. This type of finding is suitable for SMEs beginners, refinancing existing loans, growth financing, mergers and acquisitions. (shrink)

The market for environmental, social and governance (ESG) rating and data is expected to grow immensely in the next decades. This brief zooms in both on the integration of ESG risks into “traditional” operations of Credit Rating Agencies (CRAs) as well as on the emergence of a market exclusively dedicated to ESG rating. As will become clear, the market for ESG ratings suffers from serious shortcomings, a lack of transparency and standardisation, significant biases and conflicts of interests, and a (...) dependence on large, oligopolistic, credit rating agencies. (shrink)

Philosophers of science and metascientists alike typically model scientists’ behavior as driven by credit maximization. In this article I argue that this modeling assumption cannot account for how scientists have a default level of trust in each other’s assertions. The normative implication of this is that science policy should not focus solely on incentive reform.

The objective of this paper is to examine the role of the banking system in the growth of the Vietnamese economy in the process of the transition that started in the early 1990s. An ARDL approach-based multivariate regression technique is applied to shed light on the impact on the growth of banking development, which is measured by broad money and bank credit. The empirical findings confirm a positive long-term effect of banking development on growth, reflecting the important role of (...) the banking system in a typical bank-based financial system in mobilizing and supplying capital to the economy, thus contributing to growth throughout the process of economic transition. The empirical findings also indicate a nonlinear effect and a diminishing marginal effect of banking development in the sub-period 2007–2020. The thresholds for the two measures of banking development are estimated to be around 107% and 101% of the GDP, respectively. This finding suggests that bank credit expansion needs to be closely controlled to be adaptive to the capital-absorptive capacity of the economy. To a certain extent, this finding is also an indicator of the ongoing extensive growth model adopted in Vietnam, which relies heavily on the quantity of invested capital. (shrink)

Libertarian self-ownership views in the tradition of Locke, Nozick, and the left-libertarians have supposed that we enjoy very powerful deontological protections against infringing upon our property. Such a conception makes sense when we are focused on property that is very important to its owner, such as a person’s kidney. However, this stringency of our property rights is harder to credit when we consider more trivial infringements such as very mildly toxic pollution or trivial risks such having planes fly overhead. (...) Maintaining that our rights against all infringements are very powerful threatens to implausibly make such pollution and trivial risk broadly impermissible. This paper suggests that self-ownership views have tended to inappropriately conflate the seriousness of different types of infringements and that treating all infringements so seriously is implausible because it would make too much impermissible. I consider several ways to avoid this result within a self-ownership framework and conclude that the best approach is to allow that the strength of the protection against infringements should be tied to the seriousness of the harm of the infringement. (shrink)