Risks, Volume 11, Issue 1 (January 2023) – 21 articles

The purpose of this paper is to investigate equity-linked death benefits for joint alive and last survivor individuals. Utilizing Farlie–Gumbel–Morgenstern (FGM) type dependency modeling framework, we first analyze the joint distribution of the couple (joint alive and last survival density) when marginal distributions follow mixed exponentials and weighted exponentials distributions. Then, we derive the price of the guaranteed minimum death benefit (GMDB) product. In addition, we provide closed analytical expressions of the price of some financial contingent claim contracts (classical and exotic options). Furthermore, we present some numerical results to support our theoretical results. We show in our numerical example that it is important to model the dependency between two lives (couple) since the price changes as the copula parameter changes. Full article

Artificial intelligence (AI) is a tool that financial intermediaries and insurance companies use or are willing to use in almost all their activities. AI can have a positive impact on almost all aspects of the insurance value chain: pricing, underwriting, marketing, claims management, and after-sales services. While it is very important and useful, AI is not free of risks, including those related to its robustness against so-called adversarial attacks, which are conducted by external entities to misguide and defraud the AI algorithms. The paper is designed to review adversarial AI and to discuss its implications for the insurance sector. We give a taxonomy of adversarial attacks and present an original, fully fledged example of claims falsification in health insurance, as well as some remedies which are consistent with the current regulatory framework. Full article

During the COVID-19 pandemic, technology stocks, such as FAANG stocks (Facebook, Amazon, Apple, Netflix, and Google), attracted the attention of global investors due to the vast use of technology in daily business. However, technology stocks are generally considered risky stocks; hence, efficient risk management is required to construct an optimal portfolio. In this study, we investigate the volatility spillovers and dynamic conditional correlations among the daily returns of FAANG company stocks, gold, and sharia-compliant equity to construct the optimal portfolio weights and hedge ratios during the COVID-19 pandemic period by utilizing a multivariate GARCH framework. The dynamic conditional correlations reveal that both gold and sharia-compliant equities exhibit lower correlations with FAANG stocks during the COVID-19 pandemic, implying opportunities for portfolio diversification. The findings indicate that gold and shariah-compliant equity are good candidates to hedge FAANG stocks. These findings are highly relevant for international investors, asset managers, hedgers, and portfolio managers. Full article

In this study, we analyse a large sample of Egyptian social pension data which covers, by law, the policyholder’s spouse, children, parents and siblings. This data set uniquely enables the study and comparison of pairwise dependence between multiple familial relationships beyond the well-known husband and wife case. Applying Bayesian Markov Chain Monte Carlo (MCMC) estimation techniques with the two-step inference functions for margins (IFM) method, we model dependence between lifetimes in spousal, parent–child and child–parent relationships, using copulas to capture the strength of association. Dependence is observed to be strongest in child–parent relationships and, in comparison to the high-income countries of data sets previously studied, of lesser significance in the husband and wife case, often referred to as broken-heart syndrome. Given the traditional use of UK mortality tables in the modelling of mortality in Egypt, the findings of this paper will help to inform appropriate mortality assumptions specific to the unique structure of the Egyptian scheme. Full article

The purpose of this study is to investigate the salient factors that influence Indonesian cryptocurrency owners in making their investment decision. This study employs intergroup bias, subjective norms, overborrowing, and spending control to explain cryptocurrency investment behavior. The questionnaire was collected from 309 respondents from the five largest internet user areas: Jakarta, Surabaya, Bandung, Semarang, and Medan. This study executes the research framework using binary logistic regression. The results reveal that intergroup bias and overborrowing are the most impulsive factors contributing to the cryptocurrency investment decisions over the past year. Furthermore, after November 2021, Indonesian crypto owners are more irrational in a bearish period since their investment decisions are driven by their desire to be accepted in the social group. Moreover, when they have overindebtedness, instead of solving their debt problems, they prefer to spend their money on cryptocurrency investments. The subjective norms’ influencers suggest that crypto owners not invest when the cryptocurrency price is sharply declining. The findings contribute to the dual-systems perspective and social contagion theories, enriching the empirical study regarding investment behavior. Full article

The significant role of Small and Medium Enterprises (SMEs) in the growth of the economy has been well-documented in the past few decades. Studies in literature have focused on the reasons behind the trade credit offerings and acceptance of SMEs, but empirical findings revealing the positive relationship between trade credit itself and profitability is still limited. Thus, in this paper, the trade credit effect on the profitability of SMEs from the side of supply and demand is examined. The paper focused on 38 SMEs in Amman Stock Exchange (ASE) for the years from 2009 to 2021. The obtained findings showed a positive relationship between accounts payable and profitability, which indicates that SMEs should establish long-term relationships with their suppliers to maintain credit. However, no clear relationship was found between accounts receivable and profitability, represented by ROE and ROA. Furthermore, financial leverage and size were revealed to impact the profitability of SMEs. Full article

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO₂ emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019–2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019–2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO₂ futures’ returns present high volatility at low and medium frequencies, expressly in 2015–2016, 2018, the end of 2019–2020, and 2022. WCA’s empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO₂ futures’ returns, in short, medium, and long terms, as inferred from the time–frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO₂ futures’ returns, with the Green Bond Index leading. Full article

The green bond market helps to mobilize financial sources toward sustainable investments. Green bonds are similar to conventional bonds but are specifically designed to raise money to finance environmental projects. The feature of green bonds is the existence of greenium, or the lower yield compared to “conventional” bonds of the same risk. The relevance of the paper is underpinned by the mixed evidence on the existence of ‘greenium’, especially in corporate green bond markets; there has been limited research on the topic and a narrow focus on global, US, or Chinese green bond markets. Instead, the greenium in European debt markets remains underexplored. The objective of this study is to investigate the existence of greenium and its key determinants in European corporate debt capital markets, including the local markets of the United Kingdom (UK), France, Netherlands, and Germany. The sample included 3851 corporate bonds, both green and conventional ones, between 2007 and 2021 from 33 European countries. Linear regression was applied for the analysis. The results show that the climate corporate bonds in Europe are priced at a discount to the same-risk conventional corporate bonds. The magnitude of greenium is around 3 bps. Determinants of greenium include the presence of an ESG rating and belonging to the utility and financial industry. The remaining drivers of bond yields in the European corporate debt market are the credit quality (expressed by the level of credit rating), the coupon size, the bond tenor, the market liquidity, and macroeconomic variables (growth of gross domestic product and consumer price index). For the local corporate debt markets, our results are controversial. In all markets under consideration except for the UK and the Netherlands, we did not find sustainable evidence of greenium. The results of the research lead to a better understanding of the green bond market for investors, researchers, regulators, and potential issuing companies. Full article

Simulation-based company valuations are based on an analysis of the risks in the company to be valued. This means that risk analysis is decisively important in a simulation-based business valuation. The link between risk measures, risk conception and risk axiom systems has not yet been sufficiently elaborated for simulation-based business valuations. The aim of this study was to determine which understanding of risk underlies simulation-based business valuations and how this can be implemented via suitable risk measures in simulation-based business valuations. The contribution of this study is providing guidance for the methodologically correct selection of appropriate risk measures. This will help with avoiding valuation errors. To this end, the findings were combined from risk axiom systems with the valuation equations of simulation-based business valuations. Only position-invariant risk measures are suitable for simulation-based business valuations. Full article

Insurance distributors are increasingly using robo-advisors for a variety of tasks, ranging from facilitating communication with customers to providing substantive advice. Like many other AI-empowered applications, robo-advisors have the potential to pose substantial risks that should be regulated and corrected by legal instruments. In this article, we attempt to discuss the regulation of robo-advisors from the perspective of the Insurance Distribution Directive and the draft AI Act. We ask two questions for each. (1) From a positive legal perspective, what obligations are imposed on insurance distributors by the legislation when they deploy robo-advisors in their business? (2) From a normative perspective, are the incumbent provisions within that legislation effective at ensuring the ethical and responsible use of robo-advisors? Our results show that neither the Insurance Distribution Directive nor the AI Act adequately address the emerging risks associated with robo-advisors. The rules implicated by them regarding the use of robo-advisors for insurance distribution are inconsistent, disproportionate, and implicit. Legislators shall further address these issues, and authorities such as EIOPA and national competent authorities must also participate by providing concrete guidelines. Full article

This study examined the current developments in customer due diligence (CDD) in the financial technology (FinTech) era. The study of anti-money laundering (AML) and combating financing of terrorism (CFT) gained prominence after the 2007–2009 global financial crisis (GFC), in which administrative penalties were issued to financial institutions. Faced with AML regulatory compliance issues, technological solutions were or are still being developed. Thus, several technological innovation developments have shaped the future direction of the CDD aspects in the AML/CFT sphere. A bibliometric review and meta-analysis was employed for the study. The Scopus database was utilised to generate the dataset for the study, while SciVal was applied for research metric analysis. The major findings revealed that the key research themes in this area include anti-money laundering, banks and crime, and cryptocurrency, as well as blockchain and corruption. It was also established that most of the research done in this area is focused on the United Kingdom, the United States, and China. The integration of CDD with FinTech is still an emerging area that requires interdisciplinary collaborations. Full article

The purpose of this article is to examine the academic literature about the function, structure, calculation, and weaknesses of economic value added (EVA). EVA has been used as a measure of economic profit and captures the inadequacies of using traditional rates of return. In addition, this article tackles the main residual earnings (RI) modifications used to calculate EVA. A keyword search for publications was conducted in early 2022. This study couples an inferential analysis with descriptive analyses of the existing research. The articles were sorted into different clusters based on bibliographic coupling analysis. This study identifies the main areas and current dynamics of EVA research while evaluating the quality and impact of the scientific output. Three broad themes emerged from the analysis of the cluster related to the use and application of EVA: residual income and valuation, financial performance, and performance management. In doing so, we hope to enhance the understanding and contributions of EVA research to advance its theory and practice. Full article

The development of cloud technologies enables companies to actively implement technologies for cost management and risk reduction in their financial and economic activities. The use of cloud-based models of risk management in the financial and economic activities of the enterprise will help small and medium-sized companies in the agro-industrial sector in Russia to make structural and strategic changes, as well as discover new opportunities for business expansion. The purpose of the study is to develop models for cost management and reduction of risks in the financial and economic activities of companies based on the OLAP technology for application in Russian agro-industrial enterprises. The study employs a qualitative approach based on the case study methodology. The paper discloses and substantiates the authors’ conceptual model of a cost management system that allows executives to make decisions proceeding from four types of cost prices. The distinguishing feature of the management system is the use of a digital twin, which makes it possible to manage risks at the early stages of decision-making. The application of OLAP systems improves the quality of analysis and visualization methods as part of the cost management system. In addition, the study provides practical insight into how the applied model will help small and medium-sized agro-industrial enterprises to develop different business vision strategies based on cost reduction, manage the level of risk at the early stages of decision-making, and analyze information from a geographically dispersed logistics chain of divisions (production facilities, warehouses, stores). Full article

Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case of commodities, it turns out that these generators can also be used to refine the price models by tackling the high-dimensional challenges. In this work, the synthetic time series of commodity prices produced by such generators are studied and then used to train deep hedgers on various options. A fully data-driven approach to commodity risk management is thus proposed, from synthetic price generation to learning risk hedging policies. Full article

There is a growing interest in identifying the benefits that companies may have once they disclose financial and sustainability information in integrated reports. The aim of this study is to analyze the relationship between integrated thinking and reporting (ITR) and financial risk in nonfinancial companies worldwide. Data were collected mainly from the Refinitiv Eikon database for 7111 companies from 85 countries over the period 2017–2021. The focal industries are basic materials, consumer discretionary, consumer staples, energy, healthcare, industrials, real estate, technology, telecommunications, and utilities. Panel regression was used as a statistical procedure and random effects models are preferred. Hypotheses related to signaling theory are confirmed, as companies are interested in high-quality disclosures in integrated reports, reflecting a positive outlook and reduced financial risk. Our results show a negative relationship between ITR and the weighted average cost of capital, and a positive association between the main predictor and liquidity measured by the cash ratio. In addition, designing a compensation system linked to sustainability performance leads to a reduced cost of financing through debt and equity. Robustness tests were applied to the relationship between ITR and the weighted average cost of capital; the results show that stricter board oversight and holistic stakeholder management can decrease the average cost of capital and the financial risk for the company. This research is important for stakeholders looking to improve their knowledge about integrated reports and for practitioners seeking to enhance the quality of integrated reports and reduce the financial risk of companies. Full article

In this study, we focused on the development of cooperation between partner countries, which may affect the reduction of inflationary risks for partnership participants in the context of global and urgent changes in the world. This article aims to identify the relationship between inflation indicators and various types of globalization (complex integration indices) of each of the member countries of the Shanghai Cooperation Organization (SCO) in order to develop measures to contain inflation risks in these countries. The authors used the methods of pairwise linear regression, correlation analysis, and multiple linear regression. As variables, the authors used complex indicators that characterize six types of globalization: Economic, financial, demographic, industrial, information, and political indices. The authors concluded that China and India more effectively curb inflation and are less prone to inflation risks. The inflation rate and the independent variables have a close negative correlation, which indicates a strong degree of mutual influence and has a downward effect on the consumer price index. The most significant variables that have a strong influence on the inflation rate are the factors of financial and information integration. The impact of other types of integration considered in this study is not significant. In order to reduce the level of inflationary risks, the SCO member countries most vulnerable to the price volatility of raw materials (Uzbekistan, Tajikistan, and Kyrgyzstan) are encouraged to develop trade cooperation more actively, for example, by reducing or eliminating import duties on raw materials from the SCO countries. Full article

Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights. Full article

Modern conditions for real investment are generally associated with increasing uncertainty, which is even more relevant when evaluating innovative projects. Current innovation analysis methods using a linear model are outdated. At the same time, an open interactive model of the innovation process, formed due to digitalization, allows to connect to innovations at almost any stage of their life cycle. The aim of the study is to form a methodology for the economic analysis of innovative projects implemented in the context of an open innovation model. To achieve the goal, the study defines approaches to innovation projects differentiation. The approach to the analysis methods selection is based on the decision matrix. The developed decision matrix allows to determine the location of each project as its element and to select analysis methods, considering the project’s uncertainty characteristics. The logic of the analysis methods transformation under the influence of a changing uncertainty level determines the combination of the fuzzy-set approach and the concept of real options. The implementation of the project analysis algorithm leads to the choice of an appropriate method for evaluating effectiveness and ensures that the flexible risk response concept under conditions of improbable uncertainty is taken into account when implementing the option model. Full article

This paper analyzes the joint association of emotions and knowledge in decision-making under uncertainty on a TV game show setting. The objective of this research is to understand the impact of emotions and knowledge on the preference for uncertainty (PU), which have mostly been investigated separately in Economics and Psychology until now. We examine the preference for uncertainty, proxied by a preference for gambling against a sure payoff, in 59 contestants in the TV game show “JOKER”. The data used contain individuals’ characteristics, as well as the decisions regarding the game, including the choice to carry on playing or accept a sure payoff, the level of knowledge of the topic, and the emotions experienced by the contestant. The methodology adopted includes a bivariate association between PU and knowledge and emotions, respectively. Additionally, we test a multivariate association using a Classification and Regression Tree (CART) method, which is suited for a complex nonlinear decision process that robustly mimics human decision-making. We find that preference for uncertainty increases when the contestants have a full domain or total absence of knowledge. Our results suggest, also, that emotions are the factor that only determines the preference for uncertainty when contestants have a restricted level of knowledge. Our results are robust across different proxies for knowledge and emotions and for different methodological thresholds. Results matter for investors, regulators, and policymakers, since it provides novel insights about the relative impact of knowledge and emotional status on risk behavior in general. Full article

In this paper we consider a risk model with two independent classes of insurance risks in the presence of a multi-layer dividend strategy. We assume that both of the claim number processes are renewal processes with phase-type inter-arrival times. By analysing the Markov chains associated with the two given phase-type distributions of the inter-arrival times, algorithmic schemes for the determination of explicit expressions for the Gerber–Shiu expected discounted penalty function, as well as the expected discounted dividend payments are derived, using two different approaches. Full article