Risks, Volume 10, Issue 9 (September 2022) – 17 articles

Affine mortality models are well suited for theoretical and practical application in pricing and risk management of mortality risk. They produce consistent, closed-form stochastic survival curves allowing for the efficient valuation of mortality-linked claims. We model USA age-cohort mortality data using five multi-factor affine mortality models. We focus on three-factor models and compare four Gaussian models along with a model based on the Cox–Ingersoll–Ross (CIR) process, allowing for Gamma-distributed mortality rates. We compare and assess the Gaussian Arbitrage-Free Nelson–Siegel (AFNS) mortality model, which incorporates level, slope and curvature factors, and the canonical Gaussian factor model, both with and without correlations in the factor dynamics. We show that for USA mortality data, the probability of negative mortality rates in the Gaussian models is small. Models are estimated using discrete time versions of the models with age-cohort data capturing variability in cohort mortality curves. Poisson variation in mortality data is included in the model estimation using the Kalman filter through the measurement equation. We consider models incorporating factor dependence to capture the effects of age-dependence in the mortality curves. The analysis demonstrates that the Gaussian independent-factor AFNS model performs well compared to the other affine models in explaining and forecasting USA age-cohort mortality data. Full article

Companies that are performing innovation-focused strategies or experiencing digital transformation are exposed to significant long-term risks. The untimely and inefficient management of these risks leads to the destruction of the company’s value and calls into question its survival. This is often underpinned by companies following strategic management with a short-term horizon. Such “strategic myopia” prevents timely identification and treatment of strategic risks and destroys value due to physical and intellectual capital investment restrictions. However, the existing mechanisms of setting up risk management architecture neither addresses the lengths of the horizon and the alignment of the horizon with the strategic objectives, state of the environment and stakeholder expectations nor provides the tools for evaluating the horizon of the firm’s strategy. Moreover, existing systems of evaluating short-termism rely only on financial and governance metrics and do not address environmental and social factors. We closed these gaps and developed a strategic risk-controlling mechanisms to set up the risk management architecture that expanded “conventional” risk management analysis and addressed the “strategic myopia”. We also worked out the critical tool of the mechanism—the system of key risk metrics (SKRI) aimed at assessing the degree of a company’s following of long-term strategic orientation. Finally, we tested it on a sample of Russian non-financial companies. Testing results revealed a strong and positive correlation between the management’s decision to follow a long-term strategic focus and the growth of companies’ long-term value (measured by economic value added (EVA)). SKRI can be utilized in strategic risk controlling to assess the company’s propensity to follow a short-term horizon, evaluate its ability to maintain sustainable value creation, and develop recommendations to stakeholders to expand its strategic focus. Full article

The bonus-malus system (BMS) is one of the most widely used tools in merit-rating automobile insurance, with the primary goal of ensuring that fair premiums are paid by all policyholders. The traditional BMS is dependent only on the claim frequency. Thus, an insured person who makes a claim with a small severity is penalized unfairly compared to an individual who makes a large severity claim. This study proposes a model for estimating the bonus-malus premium by employing a limit value (monetary unit) which distinguishes claim size into small and large based on claim frequency and claim severity distributions. This assists in determining the penalties for policyholders with claim sizes falling above and below the limit value. The number of claims is assumed to follow a Poisson distribution, and the total number of claims with a size greater than the limit value is considered a binomial distribution. The underlying risk of each policyholder is assumed to follow a beta Lindley distribution and is referred to as the prior distribution. Each policyholder’s claim size is also assumed to follow a gamma distribution, with the Lindley distribution considered as the prior distribution. Bonus-malus premiums are calculated following the Bayesian method. Practical examples using an actual data set are provided, and the results generated are compared to those produced using the traditional Poisson binomial-exponential beta model. This methodology provides a more equitable mechanism for penalizing policyholders in the portfolio. Full article

When the uni-variate risk measure analysis is generalized into the multi-variate setting, many complex theoretical and applied problems arise, and therefore the mathematical models used for risk quantification usually present model risk. As a result, regulators have started to require that the internal models used by financial institutions are more precise. For this task, we propose a novel multi-variate risk measure, based on the notion of the Wasserstein barycenter. The proposed approach robustly characterizes the company’s exposure, filtering the partial information available from individual sources into an aggregate risk measure, providing an easily computable estimation of the total risk incurred. The new approach allows effective computation of Wasserstein barycenter risk measures in any location–scatter family, including the Gaussian case. In such cases, the Wasserstein barycenter Value-at-Risk belongs to the same family, thus it is characterized just by its mean and deviation. It is important to highlight that the proposed risk measure is expressed in closed analytic forms which facilitate its use in day-to-day risk management. The performance of the new multi-variate risk measures is illustrated in United States market indices of high volatility during the global financial crisis (2008) and during the COVID-19 pandemic situation, showing that the proposed approach provides the best forecasts of risk measures not only for “normal periods”, but also for periods of high volatility. Full article

The business environment is characterized by a high degree of uncertainty and risk. This primarily requires using resources additional to those that can be obtained from profit. The purpose of the study is to assess the impact of a risk management system on the investment attractiveness of an enterprise. Structurally, the study consisted of three stages. At the first stage of the study, the selected information was grouped depending on the type of documents. The first group included statistical data, indicating the source of the data obtained. The second group of documents included scientific research on the characteristics of the risk management system at enterprises and investment attractiveness. At the second stage of the study, an analysis of enterprises was carried out with the calculation of a correction factor, which determined the possibility of obtaining a loan. At the third stage, an indicator of the effectiveness of the risk management complex was determined. The authors revealed two classes of factors influencing the decision to issue borrowed funds, namely, the parameters of the very enterprise and the parameters of the financed project. It is proposed to divide each of the presented classes into three groups: general reports; consolidated data on the personnel, management, and owners of the enterprise; and reports directly related to risk management. Expert analysis of the identified additional factors influencing the decision to issue borrowed funds supported the conclusion that the group of factors that directly relate to the risk management system has the greatest impact. The analysis of the correspondence of the number of points scored by enterprises according to existing methods and adjusted considering the effects of the identified additional factors gave reason to state that the presence of well-established risk management increases the investment attractiveness of the enterprise. It is revealed that using the methodology for assessing the effectiveness of risk management based on the ratio of the difference in the financial capabilities of the enterprise and the costs of risk management, reduced by the amount of expected damage after the implementation of risk management to the difference in the financial capabilities of the enterprise and the costs of risk management, allows for increasing the investment attractiveness of such enterprises as the Moscow Plant of High-Voltage Fittings JSC and the Moscow Instrumental Plant JSC. Full article

In recent years, modern society has faced a number of challenges related to the achievement of global goals for sustainable development. Industrial enterprises are challenged to generate, stimulate, and demand changes in networks and supply chains, but these challenges require flexibility and innovation activity in different directions. The data for Bulgaria show that the country is last among the countries of the European Union in terms of the creation and implementation of eco-innovations. Despite this result, the pace at which the country is developing shows that in the next few years, Bulgaria has the potential to move from a modest to a moderate eco-innovator, provided that it succeeds in filling the structural gaps in the system of ecological innovation. These gaps are related not only to the need for changes in the investment of resources but also to the need for changes in individual and related systems such as science and innovation, support for SMEs, the energy system, etc. Most of the research on sustainable innovation and eco-innovation has, however, focused on firm innovation models dominated by short-term profit-maximizing approaches. Therefore, there is a need to conduct research and propose adequate strategies for modern business environments and design models that facilitate the implementation of eco-innovations in industrial enterprises. The purpose of this report is to investigate the factors influencing the development of eco-innovation activities of Bulgarian industrial enterprises, examining how they can help to achieve success through eco-innovation and improve business results. A factorial model is proposed, through which the relationships between technological, financial, organizational, informational resources, research and development activities (R&D), and company cooperation are analyzed. The PLS structural equation modeling technique was used to validate the proposed theoretical model. The survey was conducted among 380 industrial enterprises from all over the sectors of the economy in Bulgaria with the help of a specially developed questionnaire within the period of April 2019 to December 2021. The obtained results show that human resources, financial resources, and cooperation positively influence research and development activities. In addition, the achievement of a positive effect on the management of eco-innovations affects the innovation activities of industrial enterprises, their ability to carry out research and development activities, as well as their ability to manage the technical and technological resources at their disposal effectively. Finally, the innovation activity aimed at carrying out scientific research and development activity, products and processes obtained as a result of the eco-innovation activity, and adequate information management directly affect the efficiency of business processes and financial results. Full article

The activities of economic entities of the public sector in the conditions of uncertainty are associated with many risks, which manifest themselves in negative consequences, i.e., the lower effectiveness of executing assigned powers. Many subjective and objective factors influencing managerial decisions require the knowledge of methods for assessing and managing risks in order to reduce their consequences and achieve the strategic goals of economic entities. There is a promising theory of risk management that does not have the proper theoretical and methodological support, which limits its application. Currently, most studies are concerned with the feasibility of applying and adapting control mechanisms in the public sector. However, the theory of risk control, or risk management, is not considered by modern economists. The objective is to form a budget management mechanism in the Federal Treasury (regulatory body) as an element of risk control. The study considers the optimality and efficiency of the distribution of budgetary funds in the process of exercising the assigned budgetary powers by regulatory authorities and conducting a comprehensive analysis of causes and conditions that affect negative deviations from the standard values. The results of this study contribute to the body of knowledge about risk management, and the proposed approach can be used in similar studies in the public sector. Full article

This paper aims to assess the effect of corporate governance mechanisms, including board members’ and audit committee members’ characteristics, particularly their independence, expertise in terms of finance and industry and efforts on the level of fraud and money laundering (ML) in financial statements of the listed firm on the Tehran Stock Exchange. The procedure of the study is descriptive correlation based on published information from firms listed on the Tehran Stock Exchange from 2014 to 2020, using a sample of 154 firms with 1071 observations. The method used for hypothesis testing is linear regression using panel data. The Benish model is used measure the level of fraud in financial statements, and for ML, the auditors’ opinion are used. The results show that board characteristics, including independence, financial expertise, industry expertise and board effort, as well as audit committee features, such as independence, financial expertise, industry expertise and audit committee effort, have a significant and negative impact on the fraudulent financial reporting and ML. Moreover, since this paper was carried out in an emerging financial market, particularly in Iran, to figure out the effect of corporate governance structures on financial statement fraud and ML, it can provide helpful information for investors and policymakers in this regard. Full article

In the past years, there has been an extensive investigation of the class of stochastic volatility models for the evaluation of options and complex derivatives. These models have proven to be extremely useful in generalizing the classic Black–Scholes economy and accounting for discrepancies between observation and predictions in the simple log-normal, constant-volatility model. In this paper, we study the structure of an options market with a stochastic volatility that will eventually vanish (i.e., reaches zero) for very short periods of time with probability of one. We investigate the form of pricing measures in this situation, first in a simple binomial case, and then for a diffusion model, by constructing a weak approximation in discrete space and continuous time. The market described allows fleeting arbitrage opportunities, since a vanishing volatility prevents the construction of an equivalent measure, so that pricing contingent claims are, a priori, not obvious. Nevertheless, we can still produce a fair pricing equation. Let us note that this issue is not only of theoretical relevance, as the phenomenon of very low volatility has indeed been observed in the financial markets and the economy for quite a long time in the recent past. Full article

The avenue to find a balanced assessment of systemic financial institutions needs the integration of macro and micro granular datasets. This paper investigates how macroeconomic shocks affect systemic risk through several transmission channels. Employing Indonesia datasets over 2008–2019, we regressed three market models: CoVaR, MES, and SRISK using fixed effect, random effect, GARCH(1,1), and finite mixture models. The findings show that stock beta, market index, and exchange rate volatility amplify the systemic risk while the liquidity spread outcome varies due to different of model variables and the deepness of the country’s financial market. We propose a practical systemic risk assessment framework and samples of technical integration to capture the overall risk endogenously and externally expose the systemically important financial institutions. Full article

Failure to holistically manage risk in Small Medium Enterprises (SMEs) is one of the major causes of small businesses failure. To answer the research question as to what supports the adoption of Enterprise Risk Management (ERM) in SMEs, this research aims to analyse Risk Management (RM) in SMEs and develops a framework to facilitate the adoption of ERM. In achieving the primary objective, the research establishes for SMEs: the sources of information for RM; the importance of information governance in managing risk; the fundamentals of RM; and the pillars of RM. Previous research conducted on RM in SMEs reviewed the challenges of the successful implementation of ERM in SMEs and proposed different ways to address these challenges. The common ground reached by the research is that there is a need for the simplification of ERM in SMEs. We followed an interpretive philosophy with an inductive research approach and employed a qualitative methodological choice with a cross-sectional time horizon through data collection, employing a review of the scholarly literature, to, in the end, develop a conceptual Small Medium Enterprises Risk Management Framework (SMERMF). The limitation of the research is that the empirical part of the research has not been concluded yet. To present the results, that will be compared to the theory and conclude the research. Full article

This article develops a conceptual framework, based on a comprehensive literature review, to address illicit financial flows (IFFs), characterised by the illegal move of monies or capital across country borders. IFFs compromise transparency through complex transactions and incur harmful effects for both developing and developed economies. Financial opacity creates a conducive environment for IFFs to flourish, as a challenge to good financial practices. Following an interpretive philosophy, an inductive research approach, qualitative methodological choice, cross-sectional time horizon all through data collection through review of scholarly literature, and framework were developed to analyse the said IFFs. Our framework encourages good corporate governance and provides insights, as well as the identification of possible characteristics of IFFs perpetuated in the financial statements of entities, which would discourage entities to engage in IFFs. Specifically, practitioners should be able to identify characteristics of IFFs and use the framework to address these. Within the finance dimension, it is important to study the specific mechanisms regarding how IFFs may damage an entity’s reputation, as well as their going concern. In future work, we shall enhance the framework through interviews with auditors, followed by a validation of the enhanced framework through a focus group. The utility of the final framework can be tested through case studies in the industry to analyse IFFs. Full article

Estimating outstanding claims reserves in the non-life insurance business is often impaired by outlier-contaminated datasets. Widely used methods to eliminate outliers in non-life development triangles are either limiting the number of outliers by robust statistical methods or by change of development factors. However, the whole estimation process is likewise adversely affected so that: (i) the total sum of all triangle payments is not correct or (ii) the difference between the original triangle and its backward estimation via the bootstrap method is ineligible. In this paper, the properties of the heat equation are examined to obtain an outlier smoothing technique for development triangles. The heat equation in two dimensions is being applied on an outlier contaminated dataset where no individual data are available. As a result, we introduce a new methodology to (i) treat outliers in non-life development triangles, (ii) keep the total sum of all triangle payments, and (iii) provide acceptable differences between the original and the backward estimated triangle. Consequently, the outlying values are eliminated and the resulting development triangle could be used as an input for any claims reserving method without a need for further robustification or change of development factors. Additionally, the research on the application of heat equation in one dimension presented in this paper enables one to employ the bootstrap method using Pearson’s residuals in cases where the method was originally inapplicable due to development factors being lower than one. Full article

We find that the readability of financial footnotes and risk management committees contributes to audit fees. We use observations from 758 companies listed in Indonesia for 2014–2018, and moderated regression analysis is used for statistical analysis. The results show that the readability of financial footnotes will affect audit fees paid, and RMC strengthens the relationship between the readability of financial footnotes and audit fees. In addition, we also used robustness assays to address endogeneity issues with consistent results as moderated regression analysis (hereafter MRA). These findings provide evidence for policymakers about the relationship between the readability of financial footnotes, RMC, and audit fees. Full article

Forecasting the creditworthiness of customers is a central issue of banking activity. This task requires the analysis of large datasets with many variables, for which machine learning algorithms and feature selection techniques are a crucial tool. Moreover, the percentages of “good” and “bad” customers are typically imbalanced such that over- and undersampling techniques should be employed. In the literature, most investigations tackle these three issues individually. Since there is little evidence about their joint performance, in this paper, we try to fill this gap. We use five machine learning classifiers, and each of them is combined with different feature selection techniques and various data-balancing approaches. According to the empirical analysis of a retail credit bank dataset, we find that the best combination is given by random forests, random forest recursive feature elimination and random oversampling. Full article

This study aims to test the adaptive market hypothesis by using the myopic behavior of investors as a new proxy. The data have been taken from New York Stock Exchange from December 1994 to December 2020. Following this collection of data, the companies’ stock prices were distributed into six different portfolios based on size, investment, a book-to-market value, and operating profit. Ordinal logistic regression was used to calculate the probability of recovery of losses after experiencing a decline in the market. As part of the robustness analysis, this study replaces the Sharpe ratio with a Lower Partial Moment ratio. Most of the results for the Sharpe ratio and Lower Partial Moment ratio are similar. During 1995–1999, 2002–2006, and 2010–2020, the investors have not shown myopic behavior towards losses, but from 2000–2001 and 2007–2009 the investors exhibited myopic characteristics. Furthermore, as investors move between myopic and non-myopic loss aversion, the study reports that the US market is both efficient and inefficient at various points in time, following the adaptive market hypothesis. Thus, such a finding could act as a basis for future investment models by adapting traditional models or by building and contributing to the development of new ones. Full article

The purpose of this study is to examine whether FinTech companies believe that the growing dependence on regulation represents a potential risk for their development. In 2021, we conducted a survey among Latvian FinTech companies to ascertain their attitude toward regulatory scrutiny. We received 31 responses, representing a 33% response rate. The responses show that regulation is still one of the most pressing issues for FinTech companies, even though it is not necessarily regulation per se that causes concerns, but the lack of a regulatory framework that would be suitable for the special situation of the FinTech sector. However, regulation is now regarded as less problematic than it was in a previous survey in 2019, when respondents saw regulation as the most pressing issue. Moreover, the FinTech industry anticipates better support from the regulator, such as more realistic sandbox approaches and a willingness to consider new business models. According to the survey responses, the UK, Estonian, and Lithuanian regulators can serve as inspiration in this regard. Latvian FinTech companies expect regulators to be more flexible and open in their communication. This study is intended to advance regulatory reform by aiding the understanding of the requirements of fast-evolving FinTech companies. Full article