Risks

We find the asymptotics of the value function maximizing the expected utility of discounted dividend payments of an insurance company whose reserves are modeled as a classical Cramér risk process, with exponentially distributed claims, when the initial reserves tend to infinity. We focus on the power and logarithmic utility functions. We also perform some numerical analysis. Full article

Pension entitlements are influenced by individual career paths and labor market conditions, which often result in gender-based disparities. Women face several challenges during their working lives, such as late entry into the labor market, the gender pay gap, discontinuous working careers, and early retirement due to family caregiving, which lead to lower pension incomes. This paper investigates the gender pension gap in nine European Union countries from 2004 to 2020. Our study adopts a non-parametric estimation strategy that utilizes additively decomposable inequality measures to provide a more informative perspective on gender inequality. We aim to demonstrate that this approach surpasses the standard gender gap in pension index in capturing between-gender inequality in societies. Employing data from the SHARE database, we find that gender inequality in the studied countries is decreasing on average, with a convergence trend observed from 2011 onwards. This study contributes to a more comprehensive understanding of the gender pension gap phenomenon, which is crucial for developing effective policy responses in a welfare perspective. Full article

We propose a model for risk adjustment, in the context of IFRS 17, for surrender risk. Surrender rates are assumed to follow a stochastic process, underpinned by data. The distribution of the present value of future individual cash flows is calculated. Using well-known techniques from the theory of convex ordering of stochastic variables, we present closed formula approximations of risk measures, such as quantiles, for the total portfolio. These formulas are easy to program and enable an insurance company to calculate its risk adjustment without time-consuming simulations. Full article

We present a pathwise deep Backward Stochastic Differential Equation (BSDE) method for Forward Backward Stochastic Differential Equations with terminal conditions that time-steps the BSDE backwards and apply it to the differential rates problem as a prototypical nonlinear problem of independent financial interest. The nonlinear equation for the backward time-step is solved exactly or by a Taylor-based approximation. This is the first application of such a pathwise backward time-stepping deep BSDE approach for problems with nonlinear generators. We extend the method to the case when the initial value of the forward components X can be a parameter rather than fixed and similarly to also learn values at intermediate times. We present numerical results for a call combination and for a straddle, the latter comparing well to those obtained by Forsyth and Labahn with a specialized PDE solver. Full article

The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines, sourced from the ScienceDirect.com, ProQuest, and Scopus databases. A bibliometric analysis was conducted to determine the study characteristics and identify any research gaps. The results of the analysis show that studies on this topic are rarely carried out. Research in this field is generally performed only in univariate cases and is very complicated in multivariate cases. Given these limitations, further research could focus on developing a conceptual model that is dynamic and sensitive to extreme fluctuations, with multivariable inputs, in order to predict investment risk. The model developed here considered the variables that affect stock price fluctuations as the input data. The combination of VaR–EVT and machine-learning methods is effective in increasing model accuracy because it combines linear and nonlinear models. Full article

The International Financial Reporting Standard (IFRS) 9 relates to the recognition of an entity’s financial asset/liability in its financial statement, and includes an expected credit loss (ECL) framework for recognising impairment. The quantification of ECL is often broken down into its three components, namely, the probability of default (PD), loss given default (LGD), and exposure at default (EAD). The IFRS 9 standard requires that the ECL model accommodates the influence of the current and the forecasted macroeconomic conditions on credit loss. This enables a determination of forward-looking estimates on impairments. This paper proposes a methodology based on principal component regression (PCR) to adjust IFRS 9 PD term structures for macroeconomic forecasts. We propose that a credit risk index (CRI) is derived from historic defaults to approximate the default behaviour of the portfolio. PCR is used to model the CRI with the macroeconomic variables as the set of explanatory variables. A novice all-subset variable selection is proposed, incorporating business decisions. We demonstrate the method’s advantages on a real-world banking data set, and compare it to several other techniques. The proposed methodology is on portfolio-level with the recommendation to derive a macroeconomic scalar for each different risk segment of the portfolio. The proposed scalar is intended to adjust loan-level PDs for forward-looking information. Full article

This paper aims to investigate the impact of blockchain on the quality of the information in listed and non-listed companies in Iraq; the temporal scope of this study is 2022. The statistical population of this research is divided into two parts: one part is related to the level of familiarity with blockchain technology of accountants, independent auditors, managers, etc., and the other part is related to the effect of blockchain technology on the quality of accounting information. The sample size is determined based on Cochran’s formula, among which 1528 respondents were selected as a sample size. The results of the hypothesis testing showed that in both listed and non-listed companies, familiarity with blockchain technology had increased the quality of information. In this way, blockchain technology has positively and significantly impacted the quality of accounting information. This means that the impact of IT (Blockchain) on the quality of accounting information is the same for Iraqi listed and non-listed companies. Since the current research has been investigated in an emerging market such as Iraq, it can bring helpful information to readers in this field. Full article

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions. Full article

Traditionally, Parisian ruin is said to occur when the insurer’s surplus process has stayed below level zero continuously for a certain grace period. Inspired by this concept, in this paper we propose a modification by assuming that once a grace period has been granted when the surplus becomes negative, the surplus level will not be monitored continuously in the interim, but instead it will be checked at the end of the grace period to see whether the business has recovered. Under an Erlang distributed grace period, a computationally tractable formula for the Gerber–Shiu expected discounted penalty function is derived. Numerical examples regarding the modified Parisian ruin probability are also provided. Full article

The purpose of this study was to analyze the impact of financial performance on the tone used in the chief executive officer (CEO) statements of South Africa’s (SA) top 40 JSE-listed companies in the 2021 financial year. This study implements the quantile regression analysis and the generalized linear regression model. To perform this assessment, the integrated annual reports (IARs) containing the CEO and annual financial statements for the top 40 JSE-listed companies were extracted from their official websites. The tone level in CEO statements was determined using Azure Machine Learning (AML). This study’s findings reveal that financial performance has a positive impact on the tone of CEO statements of the top 40 JSE-listed companies, i.e., as financial performance improves, the positive tone in CEO statements also increases. Additionally, results revealed that moderately and extremely profitable companies use a more positive tone. It is recommended that users of financial statements should carefully scrutinize the tone used in CEO statements, to identify whether or not it is aimed at concealing poor performance or motivated by good performance. The study contributes to the limited tone-management literature in developing countries and in SA in particular. The computerized techniques offered by both the Statistical Package for Social Sciences (SPSS) and AML secures the validity and reliability of the content analysis, therefore, the study’s shortcomings do not compromise the generalizability of the results. The study’s sample truly represents all of the JSE’s listed companies. Full article

Board members and the chairman of the board must provide shareholders and other stakeholders with annual reports that include the chairman’s statement. The statement provides an important message to stakeholders concerning financial performance, non-financial information and future outlook of the company. Stakeholders are concerned about the transparency and usefulness of the disclosed as this would have an impact on whether the chairman’s message is readable or not. The purpose of this study is to evaluate whether messages from the chairman of the board are readable or not. A sample of 40 Johannesburg Stock Exchange listed companies, for the financial period ending 2021, was selected to meet the study objectives. The Gunning Fog Index (Fog index) was applied to assess the readability of the chairman’s statement. The study found that it was difficult to read the chairman’s statements for the selected corporations and South African companies. Full article

This study uses a panel data regression model to investigate how internal and external factors affect the profitability of city commercial banks in China. The research sample consists of 16 listed city commercial banks with an unbalanced dataset covering the time period within the period of 2008–2020. A panel data regression method is utilized to investigate the factors that influence the profitability of city commercial banks in China. There are several estimation methods in panel data, and the most commonly employed models are the fixed effects and random effects models. The pooled OLS model is often used for comparison for panel data regression, and the appropriate model will be determined by statistical hypothesis testing. The results show that internal explanatory variables such as bank size, capital adequacy, credit quality, and operating efficiency and external explanatory variables such as province GDP and inflation have a significant impact on the profitability of city commercial banks, while liquidity has no significant effect on the bank’s profitability. The paper contributes to the relevant literature by identifying the determinants of city commercial banks’ profitability considering the latest situation of the banking sector in China and provides practical implications from the perspective of improving bank profitability, which are important for both banking management and regulators and for the municipal and state. Full article

We study the convergence of the binomial, trinomial, and more generally m-nomial tree schemes when evaluating certain European path-independent options in the Black–Scholes setting. To our knowledge, the results here are the first for trinomial trees. Our main result provides formulae for the coefficients of $1/\sqrt{n}$ and $1/n$ in the expansion of the error for digital and standard put and call options. This result is obtained from an Edgeworth series in the form of Kolassa–McCullagh, which we derive from a recently established Edgeworth series in the form of Esseen/Bhattacharya and Rao for triangular arrays of random variables. We apply our result to the most popular trinomial trees and provide numerical illustrations. Full article

In this study, we examine major cryptocurrencies, present notable fraud cases, describe fraud risks, and analyze cryptocurrency financial performance. People debate whether cryptocurrency is an investment opportunity, the new Dutch Tulip Bubble, or a giant Ponzi scheme. There have been a number of high-profile fraud cases associated with cryptocurrencies, such as the FTX scandal in late 2022, thereby making fraud a real concern to current and potential future investors. Regarding financial performance, cryptocurrencies experienced a major collapse in value in the most recent period of the study, about three times worse than the major stock market indices. While in prior periods, cryptocurrencies have significantly outperformed stock market indices, recent fraud cases and the extreme volatility of cryptocurrencies indicate that investing in cryptocurrencies comes with much higher risk than traditional stock market investments. The debate over the investment potential of cryptocurrencies continues, whether they have long term value or are simply the new Dutch Tulip Bubble. The study’s findings will be useful to investors, regulators, and academic researchers regarding the cryptocurrency industry. Full article

This study presents an easy-to-handle approach to measuring the severity of reinsurance that faces a system of dependent claims, where the reinsurance contracts are of excess loss or proportional loss. The proposed approach is a natural generalization of common reinsurance methodologies providing a conservative framework that deals with the fundamental question of how much money should a government hold to prepare for natural or human-made extreme risk events that the government will cover? Although the ruin theory is commonly used for extreme risk events, we suggest a new risk measure to deal with such events in a new framework based on multivariate risk measures. We analyze the results for the log-elliptical model of dependent claims, which are commonly used in risk analysis, and illustrate our novel risk measure using a Monte Carlo simulation. Full article

Policymakers’ attempts to prevent gambling-related harm are affected by the ‘gamblification’ of, for example, video games and investing. This review highlights related issues posed by cryptocurrencies, which are decentralised and volatile digital assets, and which underlie ‘cryptocasinos’—a new generation of online gambling operators. Cryptocurrencies can be traded around the clock and provide the allure of big potential lottery-like wins. Frequent cryptocurrency traders often suffer from gambling-related harm, which suggests that many users are taking on substantial risks. Further, the lack of regulation around cryptocurrencies and social media echo chambers increases users’ risk of being scammed. In comparison to the conventional regulated online gambling sector, cryptocasinos pose novel risks for existing online gamblers, and can also make online gambling accessible to the underage, the self-excluded, and those living in jurisdictions where online gambling is illegal. Researchers and policymakers should continue to monitor developments in this fast-moving space. Full article

Logistic regression is a very popular binary classification technique in many industries, particularly in the financial service industry. It has been used to build credit scorecards, estimate the probability of default or churn, identify the next best product in marketing, and many more applications. The machine learning literature has recently introduced several alternative techniques, such as deep learning neural networks, random forests, and factorisation machines. While neural networks and random forests form part of the practitioner’s model-building toolkit, factorisation machines are seldom used. In this paper, we investigate the applicability of factorisation machines to some binary classification problems in banking. To stimulate the practical application of factorisation machines, we implement the fitting routines, based on logit loss and maximum likelihood, on commercially available software that is widely used by banks and other large financial services companies. Logit loss is usually used by the machine learning fraternity while maximum likelihood is popular in statistics. Depending on the coding of the target variable, we will show that these methods yield identical parameter estimates. Often, banks are confronted with predicting events that occur with low probability. To deal with this phenomenon, we introduce weights in the above-mentioned loss functions. The accuracy of our fitting algorithms is then studied by means of a simulation study and compared with logistic regression. The separation and prediction performance of factorisation machines are then compared to logistic regression and random forests by means of three case studies covering a recommender system, credit card fraud, and a credit scoring application. We conclude that logistic factorisation machines are worthy competitors of logistic regression in most applications, but with clear advantages in recommender systems applications where the number of predictors typically outnumbers the number of observations. Full article

Environmental challenges often present businesses with unexpected situations, and in order to address them, innovation in the direction of sustainability must become an unavoidable activity. This entails the transformation and development of the existing business models, assuming a great business risk. The occurrence of the risk and its extent can only be estimated, which is why it is important to have management models that are able to handle the challenges posed by new, constantly arising risk factors. We analyzed the largest companies based on the number of employees with headquarters or sites in Hungary with regard to the management methods used by them to manage environmental risks. The methods used were the analysis of variance and cluster analysis. Based on the results of the research it is clear that the companies surveyed are already very concerned with environmental opportunities and risks, and they expect that the role of innovations applied to manage them to play a more prominent role in their future target system. However, the level of this is significantly different and does not depend on the financial performance, and at the same time companies can be divided into distinct groups according to the level of environmental risk management. Full article

Though ample empirical evidence demonstrates the relationship between objective financial literacy and retirement planning, we have a limited understanding of the role of individuals’ subjective financial literacy in their retirement planning. In this study, we examine how individuals’ financial literacy confidence bias affects their retirement planning behaviors using survey data in China. Based on the difference between respondents’ subjective and objective financial literacy from survey data, we construct measures of individuals’ financial literacy overconfidence and underconfidence for empirical analysis. Our results document the critical role of individuals’ assessment of financial literacy in their retirement planning. We find that individuals’ financial literacy overconfidence (underconfidence) significantly promotes (demotes) their retirement planning behaviors. Full article