Risk Management Tools Research Articles

Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015–2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021–2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021–2030), the shoreline will be affected by periods of erosion (2031–2040) and then accretion (2041–2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected.The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

Risk preferences, preventive behaviour, and the probability of a loss: Empirical evidence from the COVID-19 pandemic

A theoretical model of optimal choice under risk, in which an individual chooses the level of prevention to avoid a loss, has the ambiguous prediction that a higher risk-taking preference increases the probability of a loss. To empirically investigate the prediction in the case of COVID-19 with individual-level survey data. Survey data from the Understanding America Study (UAS). The UAS Coronavirus Tracking Survey followed 8628 respondents from March 2020 until July 2021 (29 survey waves) and data was gathered on having contracted COVID-19, vaccination, and preventive behaviour. Separate UAS modules gathered data on individuals' risk preferences; twice before and once during the COVID-19 pandemic. UAS also gathered data on pre-pandemic health and socio-economic status. Combining these data, and dropping missing observations, provided longitudinal data for 4335 respondents (96,370 observations) of whom 530 contracted COVID-19. In support of the theoretical prediction, the empirical findings show that a one-standard deviation higher risk-taking preference is associated with about a one-third higher probability of contracting COVID-19 within two weeks. Furthermore, the findings show that individuals' risk-taking preference is negatively associated with the preventive behaviour of social distancing and not associated with getting vaccinated. There is, however, no support for preventive behaviour being associated with the probability of contracting COVID-19. The exception is for being vaccinated, which is negatively associated with the probability of contracting COVID-19. The findings, therefore, do not support that the positive association of the risk-taking preference with the probability of contracting COVID-19 is mediated through observed preventive behaviour. The findings support the importance of individuals' risk-taking behaviour for contracting COVID-19 and, more generally, the importance of loss prevention as a risk management tool for individuals.

Adoption of professional indemnity insurance among estate surveying and valuation firms in Oyo State, Nigeria

PurposeErrors or negligence are inherent parts of professional services, thus necessitating the adoption of professional indemnity insurance (PII) as a risk management tool to protect professional interest. This paper sought to examine the adoption of PII among Estate Surveying and Valuation firms in Oyo State, Nigeria.Design/methodology/approachThe quantitative research methodology was adopted, and primary data were collected via questionnaires distributed to 84 purposively selected Estate Surveyors and Valuers (ESVs) who are mostly principal partners or branch managers in the study area. Sixty-three questionnaires representing 75.0% were returned and found useable. Data collected were analysed with the aid of descriptive statistics of percentages and relative importance index (RII).FindingsThe study found that the influence of foreign investors as well as the requirements of PII by the professional body as a basis for annual license renewal were responsible for higher level of awareness among the practitioners. However, about average of the firms had adopted PII with few taking professional insurance policy of as low as ₦500,000 per annum. The study recommends that NIESV/ESVARBON should sensitize, monitor and enforce the adoption of PII by ESVs as a means to increase public confidence and credibility of valuers while rendering professional services to clients.Research limitations/implicationsThe current study was limited in coverage to Ibadan thus necessitating a study with wider area of coverage of national status.Practical implicationsThe study has major implications on real estate education and practice in Nigeria. There is an urgent need for the professional body to devise means of enforcing compliance with the adoption of PII so as to be able to get the confidence of their teeming clients for subsequent patronage.Originality/valueThe paper is one among the scanty studies that provides a useful guide to real estate practitioners in developing countries towards adopting PII to shield the company from unnecessary negative exposure and financial loss.

Програми реструктуризації кредитів в умовах пандемії коронавірусу: порівняльний аналіз вітчизняної та міжнародної практики

Introduction. Loan restructuring is one of the toolkit for the resolution of problem loans. It directly affects the quality of assets, profitability and capitalization. This credit risk management tool always requires fine-tuning with the involvement of the regulator, as problem loans may be misclassified, namely restructured rather than classified as non-performing, and provisions may be inadequate to the risk, i.e. understated. Problem Statement. In the conditions of a systemic crisis, the probability of defaults on loans increases, and it is extremely important that lenders go to exactly those borrowers who maintain their viability and are ready to resume debt service immediately after the normalization of working conditions. It depends on the position of the regulator to what extent this practice is uniform among banks to prevent distortion of reporting. Along with this, reaching an agreement between the parties is always a difficult process of finding compromises and balancing interests. Purpose. To study the domestic and international experience of commercial banks and banking supervisory bodies in restructuring problem loans for borrowers who have experienced financial difficulties due to the coronavirus pandemic. Methods. The study was conducted by comparing domestic and international practice in the field of credit restructuring regulation and analyzing the disclosure of relevant information in financial statements by banks using the methods of statistical analysis of series dynamics and factor analysis of data. Content analysis was used in the review of scientific publications and reports. Results. The actions of the National Bank of Ukraine on the regulation of credit restructuring can be assessed as palliative, since they do not establish clear rules and do not guarantee the prevention of manipulations by banks, and also do not encourage creditors to grant concessions to borrowers. Banks with foreign capital (of Western countries) were guided by the instructions of the European supervisory authority EBA and the instructions of the parent bank regarding the recognition of the quality of restructured loans. Independent auditors of Ukrainian banks used different approaches to disclosure of information about restructuring in annual reports. In Ukraine, as in most countries of the world, the regulatory body gave temporary consent to the exclusion of restructured loans from the amount of problem loans. The benefit for banks is the ability not to accrue reserves for such exposures. However, the suspension of principal payments did not exempt him from paying interest, and in fact the borrower overpaid for the loan. It seems correct if the regulator would require recognition of the loan as problematic in case of repeated restructuring. Conclusions. The NBU is called upon to summarize the results of the work carried out by banks on credit restructuring and to provide banks with specific recommendations so that the banking sector would be ready for possible new upheavals It is expedient to determine 9 months as the maximum period of moratorium (credit holidays) that can be applied by banks.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities.

To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions. In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs. Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions. To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Trends in weather-related marine cargo insurance claims: A South African perspective

Background: Marine cargo insurance covers the loss or damage to goods while in transit. It is an important supply chain risk management tool for organisations involved in international trade, especially in regions where cargo insurance penetration is low, and protection gaps are high. However, marine cargo insurance faces new challenges because of increasing frequency and severity of extreme weather events. This study focuses on the case of South Africa: a developing country with a strategic role in connecting Africa with the global market.Objectives: The research analyses the trends, differences, and seasonality of weather-related marine cargo insurance claims in South Africa from 2013 to 2022.Method: Statistical and general claims analysis was performed on a master dataset of 17 727 claims to answer the three research questions related to the research objectives.Results: The results show that weather-related marine cargo insurance claims have been increasing over the past decade, creating challenges and disruptions to the supply chain network in South Africa. Furthermore, this research shows that weather-related claims have higher average values and follow a seasonal pattern compared with non-weather-related events on supply chains in South Africa.Conclusion: This research reveals the growing impact of weather-related events in South Africa.Contribution: It provides practical and theoretical implications for supply chain managers and insurers to manage weather-related risks more effectively and proactively contributes to the literature by offering empirical insights from a developing country perspective, and provides considerations for future work.

Hedging precious metals with impact investing

Our study investigates the role of impact investing as a risk management tool for precious metals (i.e., palladium, platinum, gold, and silver) in calm and turbulent times. The findings show that impact investing and precious metals portfolios have higher hedging effectiveness. Furthermore, the results show that the hedging effectiveness for the portfolio of gold and impact investing increased during the COVID–19 crisis. Finally, we find that when factoring the transaction cost in portfolios of impact investing and precious metals, investors at various levels of risk aversion gain higher utility. These findings contribute to the literature and interest of investors, policymakers, fund managers, and regulators.

Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species.

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial-temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal-vertical movement model results with spatial-temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n=28 leatherback tracks, 2004-2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle-fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) (https://www.upwell.org/sptw), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial-temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.

The Psychomotor Cognition Test for Measurement of Sleepiness/Fatigue on a Touch Screen.

The Psychomotor Vigilance Test (PVT) is a simple and reliable performance test that measures sustained attention, alertness level, and fatigue level. The PVT is a convenient tool that can be used in real time in situ through a mobile device without the assistance of experts and therefore can be used to improve safety and prevent accidents. However, the original PVT is vulnerable to the subject's intentional concentration on the test, and the variance range among tests is narrow; these factors limit its usefulness in classifying the level of fatigue. This study overcome these limitations and develop the Psychomotor Cognition Test (PCT) by transforming the PVT into a tool that stably classifies fatigue levels, still requiring a short period of time. In the PCT, compared to the PVT, reaction time is significantly longer, and success rate is significantly lower (both p<0.0001). Whereas reaction time and success rate of the PVT do not show a significant correlation with fatigue level, those of the PCT show significant correlations with fatigue level, respectively (p<0.001). This study suggests that the PCT can be used in real time in situ as a risk management tool for workers performing dangerous tasks and can become an even more powerful tool when combined with other physiological indicators.

Formation of a System for Monitoring Risks in the Implementation of Life Cycle Contracts

Objective: In light of the current economic volatility and the need for sustainable development, the objective of this study is to develop risk management tools for construction companies involved in investment and construction projects, particularly those implemented through public-private partnerships. Methods: The study examines the activities of construction companies in implementing risk management procedures for life-cycle contracts. It emphasizes the establishment of key risk indicators (KRI) and the development and implementation of measures to mitigate risks associated with life-cycle contracts, taking into account regulatory requirements and sustainable development principles. Results: The study identifies the main risks faced by construction companies in the implementation of life-cycle contracts and provides lists of KRI grouped according to these risks. The proposed risk monitoring system enables effective risk management, ensuring compliance with regulations and promoting sustainable development practices. Conclusion: The findings reveal the main activities of construction companies in the organization of risk management procedures for life-cycle contracts, including the establishment of key risk indicators, and the development and implementation of measures to mitigate the risks of life-cycle contracts.

A weighted centroids approach based trapezoidal interval type-2 fuzzy TOPSIS method for evaluating agricultural risk management tools

A weighted centroids approach based trapezoidal interval type-2 fuzzy TOPSIS method for evaluating agricultural risk management tools

How does actual relative to target leverage affect reinsurance use?

This study investigates the relationship between capital structure deviation and reinsurance use. Prior studies on capital structures and hedging focus on how actual leverage is related to risk management tools. Using a sample of U.S. property–liability insurers from 2002 to 2021 and a simultaneous equations model, we argue and find that for overleveraged (underleveraged) insurers, the higher the actual leverage, the more (less) reinsurance use. Collectively, insurers with more deviations from their target leverages tend to purchase more reinsurance. Our evidence indicates that such capital structure deviations play an important role in dictating reinsurance use. We also find that these relationships are moderated by external shocks, including financial crises, catastrophic and pandemic events. The results imply that regulators may pay close attention to not only insurers’ actual leverages level but also their capital structure deviations, which could potientilly cause financial insolvency.

Achieving Efficiency in Black-Box Simulation of Distribution Tails with Self-Structuring Importance Samplers

Scalable and efficient importance sampling for managing tail risks As the models employed in the realm of risk analytics and optimization become increasingly sophisticated, it is crucial that risk management tools, such as variance reduction techniques, that are typically designed for stylized models on a case by case basis evolve to scale well and gain broader applicability. In the paper titled “Achieving efficiency in black-box simulation of distribution tails with self-structuring importance samplers,” the authors take a step toward this goal by introducing a novel importance sampling scheme for estimating tail risks of objectives modeled with a diverse range of tools, including linear programs, integer linear programs, feature maps specified with neural networks, etc. Instead of explicitly tailoring the change of distribution for each specific model, as conventionally done, the paper identifies an elementary transformation of the samples. This transformation, when applied alike across a wide variety of models, yields a near-optimal reduction in variance for estimating/optimizing over tail expectations.

Tourism SMEs’ resilience strategies amidst the COVID-19 crisis: the story of survival

ABSTRACT Small and medium enterprises (SMEs), play a crucial role in the economy of every destination, particularly in developing countries. However, due to their size, limited resources, cash flow, and restricted access to risk-management tools, SMEs are often significantly impacted by crises. Therefore, gaining a deeper understanding of the resilience strategies they employ during and after a crisis is of paramount significance. The purpose of this study is to identify the resilience strategies utilized by tourism SMEs amidst the COVID-19 crisis in a developing country, Iran. Qualitative data was collected through interviews with tourism SME owners and managers in two phases, conducted in 2020 and 2022. This primary data was supplemented by secondary data collected during the same period. The findings of this study highlight the disproportionate impact of global crises on tourism SMEs in Iran, exacerbating the negative consequences of the country's sanctions-ravaged economy. Through offering insights into the resilience tactics adopted by SMEs, this study aims to provide both theoretical and practical contributions.

A GIS-Based Index of Physical Susceptibility to Flooding as a Tool for Flood Risk Management

The identification and classification of flood-prone areas comprise a fundamental step in the Flood Risk Management approach, providing subsidies for land use planning, floodproofing policies, the design of mitigation measures and early warning systems. To address this issue, a frequently used preliminary tool is the flood susceptibility mapping of a region using a range of widely available data. Therefore, the present study introduces an index-based approach able to qualitatively assess flood-prone areas, named Physical Susceptibility to Floods Index (PhySFI), based on a multi-criteria decision-making method and developed in a GIS environment. The methodology presupposes a critical discussion of variables commonly used in other flood indexes, intending to simplify the proposed representation, and emphasizes the role of the user/modeler. PhySFI is composed of just four indicators, based on physical parameters of the assessed environment. This index was developed and first applied in the city of Rio de Janeiro, as part of the Rio de Janeiro Climate Change Adaptation Plan. The validation process was based on a comparative analysis with flood extent and height simulated by the hydrodynamic modeling of four watersheds within the study area, with different urbanization processes for each one. The results indicate that the index is a powerful preliminary tool to assess flood-prone areas in coastal cities.

Bayesian joint modelling of life expectancy and healthy life expectancy and valuation of retirement village contract

Life expectancy and healthy life expectancy are both important indicators of population wellbeing, reflecting the expected length of life and duraiton of healthy living respectively. While extensive research has focused on projecting life expectancy, very few have investigated the projection and simulation of healthy life expectancy and examined its potential applications. In this paper, we establish a Bayesian approach to jointly model life expectancy and healthy life expectancy. We apply this approach to the population data of various countries, demonstrating its ability to provide accurate forecasts, generate probability intervals to capture future uncertainty, and estimate missing data values. We then illustrate an application of the Bayesian model in valuing a retirement village contract. Many Australian seniors choose retirement villages for the community support they offer, transitioning to aged care facilities as their health declines. The value of the accommodation service depends on the resident’s length of stay, which can be approximated by healthy life expectancy. The proposed Bayesian model can produce a sampled distribution of the contract’s net present value, providing a useful risk management tool for retirement village operators. Our findings emphasise the importance of considering healthy life expectancy in policies on public health and retirement.

Potential Welfare Gains from Optimal Macro Hedging for Oil Exporters

This paper computes the welfare gains from optimal hedging with futures contracts for an oil-exporting country. Unlike previous studies, this paper derives the welfare gains under a more realistic futures hedging model. This is accomplished by considering basis risk and by relaxing the full-hedging assumption. Furthermore, this is the first paper to derive the welfare gains under optimal hedging strategies. We also incorporate the empirical relationship between spot and futures prices within our models, rather than the theoretical relationship which most studies employ. The models were developed under a dynamic stochastic optimization framework and the optimal consumption and value functions were found using the method of Endogenous Gridpoints. The results showed that the choice of the optimal hedging strategy employed led to a slight improvement in the country’s welfare gains relative to full hedging. We also found that the strategies with the highest welfare gains were the most effective at volatility reduction. Finally, this paper provides compelling evidence for the use of optimal macro futures hedging as an effective risk management tool for oil-exporting developing countries.

The empirical demand for farm insurance in Ireland: a quantile regression approach

PurposeThe purpose of this paper is to analyse the association between farm/farmer characteristics and unsubsidized farm insurance premium expenditure in Ireland. The distribution of farm insurance expenditures is wide, and it is important to understand the extent to which individual factors influence demand for different levels of insurance premium.Design/methodology/approachThe quantile regression approach and farm accountancy data from the Teagasc National Farm Survey are used to model the association between farm/farmer characteristics and farm insurance demand in Ireland.FindingsAsset values (livestock, buildings and machinery) are positively associated with total insurance expenditure. Both forestry area and crop area are significantly associated with farm insurance expenditure with a stronger influence on the middle and upper part of the distribution. The interaction between farm income and farmer age is positively associated with insurance expenditure pointing to the importance of farm income protection.Research limitations/implicationsThe research is mainly concerned with insuring against substantive risks, which are capable of threatening the asset base and continuation of the farm business. Future research can integrate questions in relation to farm safety and farmer health with research on the economic survival of the farm business.Practical implicationsFarmers in Ireland adopt unsubsidized farm insurance as a risk management tool. This situation is relevant to other EU member states including Belgium, Denmark, Germany and Sweden. The findings can be used to inform stakeholders and policymakers about the relative impact of different factors on insurance expenditure.Originality/valuePrevious research has typically focused on the linear relationship between farm/farmer characteristics and insurance demand without accounting for variability across the size distribution. This research is based on the quantile regression approach where the association between farm/farmer characteristics and farm insurance expenditure can be assessed at different points of the distribution.

Does Music Encourage Residents to Purchase Commercial Insurance? Evidence From China

Commercial insurance is an important risk management tool whose demand is affected by psychological factors. Music can promote consumption by evoking non-random emotional and behavioral responses in consumers . Using data from the China Comprehensive Social Survey, we examined the impact of music on commercial insurance purchase behavior using a probit model, an instrumental variable, propensity score matching, and other methods. The results indicate that music can significantly increase the probability of residents purchasing commercial medical insurance and endowment insurance by 2.569% and 1.869%, respectively. Regarding the mechanism, music encourages residents to purchase insurance by increasing their general cognition, risk cognition, insurance cognition, and ability to manage risk. Furthermore, an analysis of heterogeneity shows that music plays a greater role in promoting the aforementioned behavior of residents with lower levels of education, lower frequencies of social interaction, internet usage, an agricultural household registration, and of residents with jobs.

Methodological toolkit for financial risk management in the process of business digital transformation

Nowadays, the process of digitalization has strongly entered into Ukrainian business. However, along with significant advantages, digital transformation of business cause increasing of certain risks, including financial risks. From our point of view, financial risk management approaches and tools should adapt the new circumstances. The analysis of scientific literature showed that there are no thorough studies on the use of digitalization tools in the process of financial risk management today. The purpose of the study is to develop a financial risk management model in the process of digital business transformation. In the research process, general scientific and specific methods were applied: the method of analysis, synthesis, structural logical method, method of systematization and generalization, etc. Scientific works of Ukrainian and foreign scientists on the topic of research.The proposed model of risk management in the process of digital business transformation will contribute to the optimization of the process of using digitization tools.The advantages of a substantiated model are the next: detailing of the model according to specified stages of the risk management process; taking into account the purpose of using digitization tools at each stage of the process; comprehensive nature of the developed model.