Several life contingency agreements are based on the assumption that policyholders have impaired life expectancy attributable to factors, such as lifestyle, social class, or preexisting health issues. Quantifying two crucial variables, augmented death probabilities and the discount rate of projected cash flows, is essential for pricing such agreements. Information regarding the correct values of these parameters is subject to vagueness and imprecision, which further intensifies if impairments must be considered. This study proposes modelling mortality and interest rates using a generalization of fuzzy numbers (FNs), known as intuitionistic fuzzy numbers (IFNs). Consequently, this paper extends the literature on life contingency pricing with fuzzy parameters, where uncertainty in variables, such as interest rates and death probabilities, is modelled using FNs. While FNs introduce epistemic uncertainty, the use of IFNs adds bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis focuses on two agreements involving policyholders with impaired life expectancies: determining the annuity payment in a substandard annuity and pricing a life settlement over a whole life insurance policy. In particular, we emphasize modelling interest rates and survival probabilities using triangular intuitionistic fuzzy numbers (TIFNs) owing to their ease of interpretation and implementation.
{"title":"Pricing Life Contingencies Linked to Impaired Life Expectancies Using Intuitionistic Fuzzy Parameters","authors":"Jorge de Andrés-Sánchez","doi":"10.3390/risks12020029","DOIUrl":"https://doi.org/10.3390/risks12020029","url":null,"abstract":"Several life contingency agreements are based on the assumption that policyholders have impaired life expectancy attributable to factors, such as lifestyle, social class, or preexisting health issues. Quantifying two crucial variables, augmented death probabilities and the discount rate of projected cash flows, is essential for pricing such agreements. Information regarding the correct values of these parameters is subject to vagueness and imprecision, which further intensifies if impairments must be considered. This study proposes modelling mortality and interest rates using a generalization of fuzzy numbers (FNs), known as intuitionistic fuzzy numbers (IFNs). Consequently, this paper extends the literature on life contingency pricing with fuzzy parameters, where uncertainty in variables, such as interest rates and death probabilities, is modelled using FNs. While FNs introduce epistemic uncertainty, the use of IFNs adds bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis focuses on two agreements involving policyholders with impaired life expectancies: determining the annuity payment in a substandard annuity and pricing a life settlement over a whole life insurance policy. In particular, we emphasize modelling interest rates and survival probabilities using triangular intuitionistic fuzzy numbers (TIFNs) owing to their ease of interpretation and implementation.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We obtain the upper and lower bounds for the ruin probability in the Sparre–Andersen model. These bounds are established under various conditions: when the adjustment coefficient exists, when it does not exist, and when the interarrival distribution belongs to certain aging classes. Additionally, we improve the Lundberg upper bound for the ruin probability.
{"title":"Bounds for the Ruin Probability in the Sparre–Andersen Model","authors":"Sotirios Losidis, Vaios Dermitzakis","doi":"10.3390/risks12020028","DOIUrl":"https://doi.org/10.3390/risks12020028","url":null,"abstract":"We obtain the upper and lower bounds for the ruin probability in the Sparre–Andersen model. These bounds are established under various conditions: when the adjustment coefficient exists, when it does not exist, and when the interarrival distribution belongs to certain aging classes. Additionally, we improve the Lundberg upper bound for the ruin probability.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.
{"title":"Model for Technology Risk Assessment in Commercial Banks","authors":"Wenhao Kang, Chi Fai Cheung","doi":"10.3390/risks12020026","DOIUrl":"https://doi.org/10.3390/risks12020026","url":null,"abstract":"As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"59 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on mean absolute error (MAE) and root mean squared error (RMSE) metrics to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost) demonstrates the lowest MAE, indicating higher accuracy compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Despite XGBoost’s lower MAE, it shows higher RMSE values, suggesting a broader error spread and larger magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized linear model (Poisson) showcases the best RMSE values, indicating tighter clustering and smaller error magnitudes, despite a slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error (MAE) for both frequency and severity prediction, showcasing superior accuracy. However, a generalized linear model (Gamma) offers a balance between accuracy and error magnitude, and its performance outperforms extreme gradient boosting (XGBoost) and gradient boosting machines (GBMs) in terms of RMSE metrics, with a slightly higher MAE. These findings empower insurance companies to enhance risk assessment processes, set suitable premiums, manage reserves, and accurately forecast claim occurrences, contributing to competitive pricing for clients while ensuring profitability. For cross-border trade entities, such as trucking companies and cargo owners, these insights aid in improved risk management and potential cost savings by enabling more reasonable insurance premiums based on accurate predictive claims from insurance companies.
本研究比较了跨境货物保险领域索赔频率和严重程度预测建模的模型方法。目的是在广义线性模型（GLM）和先进的机器学习技术之间确定最佳模型方法。评估重点是平均绝对误差（MAE）和均方根误差（RMSE）指标，以全面评估预测性能。在频率预测方面，极端梯度提升（XGBoost）的平均绝对误差（MAE）最低，这表明与梯度提升机（GBMs）和广义线性模型（Poisson）相比，其准确性更高。尽管 XGBoost 的 MAE 值较低，但它的 RMSE 值却较高，表明与梯度提升机器 (GBM) 和广义线性模型 (Poisson) 相比，它的误差范围更广，误差幅度更大。相反，广义线性模型（泊松）的 RMSE 值最好，表明聚类更紧密，误差幅度更小，尽管 MAE 稍高。在严重性预测方面，极梯度提升模型（XGBoost）的 MAE 值最低，表明准确性更高。不过，与广义线性模型（Gamma）相比，它的均方根误差（RMSE）更高，表明误差分散度更大。相比之下，广义线性模型（Gamma）的 RMSE 值最低，尽管 MAE 值较高，但聚类更紧密，误差幅度更小。总之，极端梯度提升（XGBoost）在频率和严重性预测的平均绝对误差（MAE）方面表现突出，显示出卓越的准确性。然而，广义线性模型（Gamma）在准确性和误差幅度之间取得了平衡，就 RMSE 指标而言，其性能优于极梯度提升（XGBoost）和梯度提升机（GBMs），但 MAE 略高。这些发现使保险公司能够加强风险评估流程、设定合适的保费、管理准备金并准确预测索赔发生率，从而在确保盈利的同时为客户提供有竞争力的定价。对于卡车运输公司和货主等跨境贸易实体来说，这些见解有助于改善风险管理和潜在的成本节约，因为保险公司可以根据准确的索赔预测收取更合理的保险费。
{"title":"A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context","authors":"Praiya Panjee, Sataporn Amornsawadwatana","doi":"10.3390/risks12020025","DOIUrl":"https://doi.org/10.3390/risks12020025","url":null,"abstract":"The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on mean absolute error (MAE) and root mean squared error (RMSE) metrics to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost) demonstrates the lowest MAE, indicating higher accuracy compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Despite XGBoost’s lower MAE, it shows higher RMSE values, suggesting a broader error spread and larger magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized linear model (Poisson) showcases the best RMSE values, indicating tighter clustering and smaller error magnitudes, despite a slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error (MAE) for both frequency and severity prediction, showcasing superior accuracy. However, a generalized linear model (Gamma) offers a balance between accuracy and error magnitude, and its performance outperforms extreme gradient boosting (XGBoost) and gradient boosting machines (GBMs) in terms of RMSE metrics, with a slightly higher MAE. These findings empower insurance companies to enhance risk assessment processes, set suitable premiums, manage reserves, and accurately forecast claim occurrences, contributing to competitive pricing for clients while ensuring profitability. For cross-border trade entities, such as trucking companies and cargo owners, these insights aid in improved risk management and potential cost savings by enabling more reasonable insurance premiums based on accurate predictive claims from insurance companies.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"11 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudio Mazzi, Angelo Damone, Andrea Vandelli, Gastone Ciuti, Milena Vainieri
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management.
{"title":"Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data","authors":"Claudio Mazzi, Angelo Damone, Andrea Vandelli, Gastone Ciuti, Milena Vainieri","doi":"10.3390/risks12020024","DOIUrl":"https://doi.org/10.3390/risks12020024","url":null,"abstract":"One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"81 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139590543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Koon Shing Kwong, Jing Rong Goh, Ting Lin Collin Chua
Loan-type reverse mortgage plans and sell-type home reversion plans for retirement financing are two well-known equity release plans that entitle homeowners not only to release cash from their properties but also to allow them to age in place. Recently, a new hybrid equity release plan was proposed to incorporate the home reversion plan’s features with an option of staying in the property for a fixed period without being subject to survival. This additional option provides flexibility to homeowners to better meet their retirement financial and personal needs by reducing the financial uncertainty of home reversion products. In this article, we propose an enhanced home reversion plan with some new features to meet retirees’ other financial needs, such as life annuity incomes and guaranteed return of principal invested. An actuarial framework is provided to analyze the cost components of each benefit offered under the enhanced home reversion product. Numerical illustrations are presented to demonstrate and examine the actuarial values of the benefits and product risks with different parameter configurations under the recent Singapore mortality data set.
{"title":"Enhancing Sell-Type Home Reversion Products for Retirement Financing","authors":"Koon Shing Kwong, Jing Rong Goh, Ting Lin Collin Chua","doi":"10.3390/risks12020022","DOIUrl":"https://doi.org/10.3390/risks12020022","url":null,"abstract":"Loan-type reverse mortgage plans and sell-type home reversion plans for retirement financing are two well-known equity release plans that entitle homeowners not only to release cash from their properties but also to allow them to age in place. Recently, a new hybrid equity release plan was proposed to incorporate the home reversion plan’s features with an option of staying in the property for a fixed period without being subject to survival. This additional option provides flexibility to homeowners to better meet their retirement financial and personal needs by reducing the financial uncertainty of home reversion products. In this article, we propose an enhanced home reversion plan with some new features to meet retirees’ other financial needs, such as life annuity incomes and guaranteed return of principal invested. An actuarial framework is provided to analyze the cost components of each benefit offered under the enhanced home reversion product. Numerical illustrations are presented to demonstrate and examine the actuarial values of the benefits and product risks with different parameter configurations under the recent Singapore mortality data set.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"7 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ivica Turkalj, Mohammad Assadsolimani, Markus Braun, Pascal Halffmann, Niklas Hegemann, Sven Kerstan, Janik Maciejewski, Shivam Sharma, Yuanheng Zhou
In this paper, we consider the inclusion of the solvency capital requirement (SCR) into portfolio optimization by the use of a quadratic proxy model. The Solvency II directive requires insurance companies to calculate their SCR based on the complete loss distribution for the upcoming year. Since this task is, in general, computationally challenging for insurance companies (and therefore, not taken into account during portfolio optimization), employing more feasible proxy models provides a potential solution to this computational difficulty. Here, we present an approach that is also suitable for future applications in quantum computing. We analyze the approximability of the solvency capital ratio in a quadratic form using machine learning techniques. This allows for an easier consideration of the SCR in the classical mean-variance analysis. In addition, it allows the problem to be formulated as a quadratic unconstrained binary optimization (QUBO), which benefits from the potential speedup of quantum computing. We provide a detailed description of our model and the translation into a QUBO. Furthermore, we investigate the performance of our approach through experimental studies.
在本文中，我们考虑通过使用二次代理模型将偿付能力资本要求（SCR）纳入投资组合优化。偿付能力 II 指令要求保险公司根据下一年的完整损失分布计算偿付能力资本要求。一般来说，这项任务对保险公司来说具有计算上的挑战性（因此在优化投资组合时没有考虑到这一点），因此采用更可行的代理模型为这一计算困难提供了潜在的解决方案。在此，我们提出了一种同样适用于量子计算未来应用的方法。我们利用机器学习技术分析了偿付能力资本比率的二次方近似性。这使得在经典的均值方差分析中更容易考虑偿付能力资本比率。此外，它还允许将问题表述为二次无约束二元优化（QUBO），从而受益于量子计算可能带来的速度提升。我们详细描述了我们的模型以及将其转化为 QUBO 的过程。此外，我们还通过实验研究调查了我们方法的性能。
{"title":"Quadratic Unconstrained Binary Optimization Approach for Incorporating Solvency Capital into Portfolio Optimization","authors":"Ivica Turkalj, Mohammad Assadsolimani, Markus Braun, Pascal Halffmann, Niklas Hegemann, Sven Kerstan, Janik Maciejewski, Shivam Sharma, Yuanheng Zhou","doi":"10.3390/risks12020023","DOIUrl":"https://doi.org/10.3390/risks12020023","url":null,"abstract":"In this paper, we consider the inclusion of the solvency capital requirement (SCR) into portfolio optimization by the use of a quadratic proxy model. The Solvency II directive requires insurance companies to calculate their SCR based on the complete loss distribution for the upcoming year. Since this task is, in general, computationally challenging for insurance companies (and therefore, not taken into account during portfolio optimization), employing more feasible proxy models provides a potential solution to this computational difficulty. Here, we present an approach that is also suitable for future applications in quantum computing. We analyze the approximability of the solvency capital ratio in a quadratic form using machine learning techniques. This allows for an easier consideration of the SCR in the classical mean-variance analysis. In addition, it allows the problem to be formulated as a quadratic unconstrained binary optimization (QUBO), which benefits from the potential speedup of quantum computing. We provide a detailed description of our model and the translation into a QUBO. Furthermore, we investigate the performance of our approach through experimental studies.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"3 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139582435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena G. Popkova, Muxabbat F. Xakimova, Marija A. Troyanskaya, Elena S. Petrenko, Olga V. Fokina
This paper is devoted to the resolution of the problem of risk management in a high-risk market environment. The goal of this paper was to study the experience of and prospects for the use of responsible innovations as tools for managing the financial risks of high-tech companies’ projects for their sustainable development (using the example of companies in Russia’s IT sphere in 2022–2023). We used the SEM method to study the daily statistics of the Moscow Exchange in 2022–2023. As a result, we quantitatively measured the financial risks of Russian companies in the IT sphere in 2022–2023. The studied case experience of the IT sphere in 2022 confirmed that Russian high-tech companies actively implement responsible innovations based on ESG projects. Our main conclusion is that the financial risks of high-tech companies are reduced in the case of the implementation of responsible innovations. Therefore, it is advisable to implement responsible innovations for the sustainable development of high-tech companies in a high-risk market environment. The theoretical significance of our conclusions lies in the substantiation of the synergetic effect of financial risk management with the help of responsible innovations. The scientific novelty and contribution of this paper to the literature consist in its clarifying the sectorial (in the IT sphere) and market (in a high-risk market environment) specifics of managing the financial risks to companies. We also disclosed a poorly studied and largely unknown unique and leading experience of managing the financial risks of Russian high-tech companies in 2022–2023. The practical significance of our recommendations is that the compiled scenario can be used as a strategic benchmark for the most complete development of the potential of the sustainable development of Russian high-tech companies in 2024.
本文致力于解决高风险市场环境下的风险管理问题。本文的目标是研究利用责任创新作为工具管理高科技公司可持续发展项目财务风险的经验和前景（以 2022-2023 年俄罗斯信息技术领域的公司为例）。我们使用 SEM 方法研究了 2022-2023 年莫斯科交易所的每日统计数据。因此，我们对 2022-2023 年俄罗斯 IT 领域公司的财务风险进行了定量测量。所研究的 2022 年信息技术领域的案例经验证实，俄罗斯高科技公司积极实施基于环境、社会和公司治理项目的负责任创新。我们的主要结论是，在实施负责任创新的情况下，高科技公司的财务风险会降低。因此，在高风险的市场环境中，为高科技公司的可持续发展实施责任创新是可取的。我们的结论的理论意义在于证实了借助责任创新进行财务风险管理的协同效应。本文在科学上的新颖性和对文献的贡献在于阐明了企业财务风险管理的行业（IT 领域）和市场（高风险市场环境）特性。我们还揭示了 2022-2023 年俄罗斯高科技公司财务风险管理的独特领先经验，而这些经验却鲜为人知。我们建议的实际意义在于，编制的方案可作为 2024 年俄罗斯高科技公司可持续发展潜力最全面发展的战略基准。
{"title":"Responsible Innovations as Tools for the Management of Financial Risks to Projects of High-Tech Companies for Their Sustainable Development","authors":"Elena G. Popkova, Muxabbat F. Xakimova, Marija A. Troyanskaya, Elena S. Petrenko, Olga V. Fokina","doi":"10.3390/risks12020021","DOIUrl":"https://doi.org/10.3390/risks12020021","url":null,"abstract":"This paper is devoted to the resolution of the problem of risk management in a high-risk market environment. The goal of this paper was to study the experience of and prospects for the use of responsible innovations as tools for managing the financial risks of high-tech companies’ projects for their sustainable development (using the example of companies in Russia’s IT sphere in 2022–2023). We used the SEM method to study the daily statistics of the Moscow Exchange in 2022–2023. As a result, we quantitatively measured the financial risks of Russian companies in the IT sphere in 2022–2023. The studied case experience of the IT sphere in 2022 confirmed that Russian high-tech companies actively implement responsible innovations based on ESG projects. Our main conclusion is that the financial risks of high-tech companies are reduced in the case of the implementation of responsible innovations. Therefore, it is advisable to implement responsible innovations for the sustainable development of high-tech companies in a high-risk market environment. The theoretical significance of our conclusions lies in the substantiation of the synergetic effect of financial risk management with the help of responsible innovations. The scientific novelty and contribution of this paper to the literature consist in its clarifying the sectorial (in the IT sphere) and market (in a high-risk market environment) specifics of managing the financial risks to companies. We also disclosed a poorly studied and largely unknown unique and leading experience of managing the financial risks of Russian high-tech companies in 2022–2023. The practical significance of our recommendations is that the compiled scenario can be used as a strategic benchmark for the most complete development of the potential of the sustainable development of Russian high-tech companies in 2024.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"147 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139582436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is growing concern that climate change poses a serious threat to the sustainability of the insurance business. Understanding whether climate warming is a cause for an increase in claims and losses, and how this cause–effect relationship will develop in the future, are two significant open questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We quantify climate change using the Spanish Actuarial Climate Index (SACI). We utilize a database containing all the claims resulting from hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, the monthly number of loss costs equal to one, and the monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We attempt to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change, as measured by the SACI, explains these three dependent variables. We also provide an estimate of the increase in the monthly total losses’ Value at Risk, corresponding to a future increase in climate change measured in units of the SACI. Spanish crop insurance managers should carefully consider these conclusions in their decision-making process to ensure the sustainability of this line of business in the future.
{"title":"Impact Assessment of Climate Change on Hailstorm Risk in Spanish Wine Grape Crop Insurance: Insights from Linear and Quantile Regressions","authors":"Nan Zhou, José L. Vilar-Zanón","doi":"10.3390/risks12020020","DOIUrl":"https://doi.org/10.3390/risks12020020","url":null,"abstract":"There is growing concern that climate change poses a serious threat to the sustainability of the insurance business. Understanding whether climate warming is a cause for an increase in claims and losses, and how this cause–effect relationship will develop in the future, are two significant open questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We quantify climate change using the Spanish Actuarial Climate Index (SACI). We utilize a database containing all the claims resulting from hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, the monthly number of loss costs equal to one, and the monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We attempt to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change, as measured by the SACI, explains these three dependent variables. We also provide an estimate of the increase in the monthly total losses’ Value at Risk, corresponding to a future increase in climate change measured in units of the SACI. Spanish crop insurance managers should carefully consider these conclusions in their decision-making process to ensure the sustainability of this line of business in the future.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"3 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139582434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of the European style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG.
世界各地的风力发电企业面临着两种风险，一是风力强度变化对能源生产的影响，二是电力零售价格的变化。为了同时规避这些风险，量化期权是一种理想的金融工具。研究中的电价自然对数将采用方差伽马（VG）和正态逆高斯（NIG）过程建模，而风速和功率序列将采用奥恩斯坦-乌伦贝克（OU）过程建模。由于风力发电量和现货价格变化带来的风险高度相关，我们必须对这种相关性进行建模。这可以通过用布朗分量替代莱维过程的小跳变，并将其与风力和风速 OU 过程相关联来重现。然后，我们将从随机的角度研究风能公司的收入，最后为风能生产商的欧式量化期权定价。我们将比较从 VG 过程和 NIG 过程得到的量化期权价格。本研究的新颖之处在于使用了一个新地理位置的新数据集，以及除 NIG 之外的新列维过程 VG。
{"title":"Stochastic Modeling of Wind Derivatives with Application to the Alberta Energy Market","authors":"Sudeesha Warunasinghe, Anatoliy Swishchuk","doi":"10.3390/risks12020018","DOIUrl":"https://doi.org/10.3390/risks12020018","url":null,"abstract":"Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of the European style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"164 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139561122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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