Risks, Volume 10, Issue 10 (October 2022) – 15 articles

In this study, we design stepwise ordinary least squares regression models using various amalgamations of firm features, loan characteristics and macroeconomic variables to forecast workout recovery rates for defaulted bank loans for private non-financial corporates under downturn conditions in Zimbabwe. Our principal aim is to identify and interpret the determinants of recovery rates for private firm defaulted bank loans. For suitability and efficacy purposes, we adopt a unique real-life data set of defaulted bank loans for private non-financial firms pooled from a major anonymous Zimbabwean commercial bank. Our empirical results show that the firm size, the collateral value, the exposure at default, the earnings before interest and tax/total assets ratio, the length of the workout process, the total debt/total assets ratio, the ratio of (current assets–current liabilities)/total assets, the inflation rate, the interest rate and the real gross domestic product growth rate are the significant determinants of RRs for Zimbabwean private non-financial firm bank loans. We reveal that accounting information is useful in examining recovery rates for defaulted bank loans for private corporations under distressed financial and economic conditions. Moreover, we discover that the prediction results of recovery rate models are augmented by fusing firm features and loan characteristics with macroeconomic factors. Full article

We propose a new approach for estimating the state-level direct and indirect economic cost of obesity in the United States for the time period 1996 to 2018. Our unique top-down methodology integrates a prevalence-based method with various medical-level costs, economic, demographic, and socio-economic factors. Using this approach, we investigate the relationship between the estimates of the total obesity-related costs and the health insurance premium by state in order to evaluate the state burden of obesity. Our estimate of the total national economic cost attributed to obesity is approximately $422 billion in 2018, representing about 2% of the national GDP for the same year. Using exponential smoothing models, we forecast that the total cost would reach $475 billion in 2021 without accounting for the impact of COVID-19 on obesity. The top states driving the cost estimates are California, Texas, New York, and Florida. A bootstrapping technique is employed to the state-level estimated cost in order to determine the average cost per person. We hope that our study will promote interest in this topic and open discussion for further research in this area. Full article

Land finance is a special land financing mode in China under the nationalization of urban land since 1954. The policy authorizes local governments to collect fiscal revenue from land grant premiums and land taxes. As China is experiencing the social and economic transformation, heavily replying on land finance starts causing financial sustainable problems. Based on the spatial panel data of 30 provinces in China in the last two decades, we analyzed the spatial-temporal evolution of land finance. We found that the spatial variation of land finance declined during the period of study and decreased from east to west. The results revealed that land finance had significant positive spatial autocorrelation and robust spatial clustering characteristics. In addition, the spatial distribution of land finance was consistent with the population-based Hu Line. We also assessed land finance risks via a four-dimensional risk matrix through spatial panel regression (SPR). The spatial spillover effects suggested that there is inter-provincial imitation and collaboration but no competition. Our forecast indicates that most provinces will be at a relatively low risk level in the next decade except some southwest provinces. Based on the findings, we highlight the policy implications to mitigate risks and maintain sustainable land finance. Full article

This study addresses the relationship between accounting quality and audit attributes (i.e., audit quality, auditor industry specialization, audit concentration, and audit fees) with companies’ SPCRs listed on the Iraqi Stock Exchange. A multivariate regression model was used to test the hypotheses. The research hypotheses were tested using a sample of 210 observations of the listed Iraqi firms from 2013 to 2018 and a multiple regression model based on the random-effects model’s panel data technique. The findings indicate a negative and significant relationship between the accounting quality, audit, auditor industry specialization and SPCRs. Results also conveyed a meaningful and positive association between stock price crash risk (SPCR) and audit fees. The results did not confirm the relationship between corporate governance and audit concentration with SPCR. The primary research model was tested with additional methods (t + 1, fixed effects, ordinary least squares). Since this is the first study addressing this issue in the emerging markets, it provides users, analysts, and legal entities with helpful information about audit attributes that significantly affect SPCR. These results also contribute to developing science and knowledge in this field and fill the literature gap. Full article

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules. Full article

Stop-loss reinsurance is a risk management tool that allows an insurance company to transfer part of their risk to a reinsurance company. Ruin probabilities allow us to measure the effect of stop-loss reinsurance on the solvency of the primary insurer. They further permit the calculation of the economic capital, or the required initial capital to hold, corresponding to the 99.5% value-at-risk of its surplus. Specifically, we show that under a stop-loss contract, the ruin probability for the primary insurer, for both a finite- and infinite-time horizon, can be obtained from the finite-time ruin probability when no reinsurance is bought. We develop a finite-difference method for solving the (partial integro-differential) equation satisfied by the finite-time ruin probability with no reinsurance, leading to numerical approximations of the ruin probabilities under a stop-loss reinsurance contract. Using the method developed here, we discuss the interplay between ruin probability, reinsurance retention level and initial capital. Full article

Mortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality and longevity risks. There is an abundance of literature on the topic of modelling and forecasting mortality, which often leads to confusion in determining a particular model to be adopted as a reliable tool. In this study, we conducted a bibliometric analysis with a focus on citation and co-citation analyses and co-occurrences of keywords to determine the most widely used stochastic mortality model. We found that the Lee–Carter model has remained one of the most relevant mortality models since its development in the 1990s. Furthermore, we also aimed to identify emerging topics and trends relating to mortality modelling and forecasting based on an analysis of authors’ keywords. This study contributes to the literature by providing a comprehensive overview and evolution of publications in stochastic mortality modelling and forecasting. Researchers can benefit from the present work in determining and exploring emerging trends and topics for future studies. Full article

In the present paper, we investigate the financial homogeneity of the euro area economies by contrasting eurozone countries’ responses to monetary policy steps to the theoretical assumptions of the liquidity trap phenomenon. Our assumption is that the euro area economies are not completely homogeneous. Hence, in a zero-interest rate environment, the asset holding decisions of economic agents exhibit detectable differences across countries. We verify our assumptions using Eurostat data. We use the financial asset stocks of the euro area countries to cluster the countries concerned. Previous literature has not examined changes in the ratio of financial assets to GDP, nor differences in structural changes in the total stock of financial assets under the zero lower bound. The paper uses k-centers cluster analysis based on Euclidean distance for detecting changes in the portfolio holdings of eurozone economic actors owing to economic crises and monetary policy responses. The results confirm that euro area financial markets are fragmented. There are significant differences across asset markets of different Eurozone countries, both during and after the crisis. Despite some similarities in the portfolio rearrangement across countries, the ECB’s monetary policy does not have a uniform impact on euro area financial markets, and notable differences prevail in the financial asset structures of the economies concerned. Full article

Stock prices are well known to exhibit behaviors that are difficult to model mathematically. Individual stocks are observed to exhibit short term price reversals and long term momentum, while their industries only exhibit momentum. Here we show that individual stocks can be modeled by simple mean reverting processes in such a way that these behaviors are captured, the model is arbitrage free, and market informational efficiency is preserved. Simulation shows that in such a market, when mean reversion is sufficiently high, strategies which use reversals would substantially outperform buy and hold strategies. Full article

In the last few years, there have been several big changes in the automotive industry, and global automotive supply chains have faced many challenges, mainly due to the COVID-19 epidemic. The virus had several huge impacts on the global market, with different risk management approaches companies and global supply chains needed to adapt to the altered situation. During the second and third wave of the epidemic, several regions and countries were under lockdown for different intervals in order to stop the spread of the virus. Some countries entered lockdown for the first time, and many of them entered lockdown again, as when the first wave occurred. The economy of the Philippines is dependent on electronics-related industries, which faced extraordinary risks from different sources, and these industries suffered severe consequences because of COVID-19. Crucial automotive suppliers outsource their production facilities to the Philippines region, and the dominant semiconductor segments were heavily impaired due to the lockdowns. Electrification in the automotive industry and the spread of electric vehicles is becoming increasingly important due to rapid technological development. The economic shock caused by COVID-19 forced companies in this sector to diversify their supply chain activities in order to stay competitive, minimize the supply chain-related risks and to start recovery processes. The authors analysed the risks, position, opportunities, challenges, difficulties, reactions and solutions of a certain automotive supplier, which was heavily reliant on the Philippines, and Chinese suppliers. Full article

We study power exchange options written on zero-coupon bonds under a stochastic string term-structure framework. Closed-form expressions for pricing and hedging bond power exchange options are obtained and, as particular cases, the corresponding expressions for call power options and constant underlying elasticity in strikes (CUES) options. Sufficient conditions for the equivalence of the European and the American versions of bond power exchange options are provided and the put-call parity relation for European bond power exchange options is established. Finally, we consider several applications of our results including duration and convexity measures for bond power exchange options, pricing extendable/accelerable maturity zero-coupon bonds, options to price a zero-coupon bond off of a shifted term-structure, and options on interest rates and rate spreads. In particular, we show that standard formulas for interest rate caplets and floorlets in a LIBOR market model can be obtained as special cases of bond power exchange options under a stochastic string term-structure model. Full article

Customer relationship management and marketing analytics have become critical for non-life insurers operating in highly competitive markets. As it is easier to develop an existing customer than to acquire a new one, cross-selling and retention are key activities. In this research, we focus on both car and household-liability insurance products and consider the time a customer owning only a single product takes before buying the other product at the same insurer. Based on longitudinal consumer data from a Swiss insurance company covering the period from 2011 to 2015, we aim to study the factors driving the duration to cross-selling. Given the different dynamics observed in both products, we separately study the car and household-liability insurance customer cohorts. Considering the framework of survival analysis, we provide descriptive statistics and Kaplan–Meier estimates along major customer characteristics, contract history and distribution channel usage. For the econometric analysis of the duration, we compare the results from Cox and accelerated failure time models. We are able to characterize the times related to the buying behavior for both products through several covariates. Our results indicate that the policyholder age, the place of residence, the contract premium, the number of contracts held, and the initial access channel used for contracting influence the duration to cross-selling. In particular, our results underline the importance of the tied agent channel and the differences along the geographic region and the urbanicity of the place of residence. By quantifying the effects of the above factors, we extend the understanding of customer behavior and provide a basis for developing models to time marketing actions in insurance companies. Full article

The synthesis of technology and finance is known as financial technology (Fintech), which brings together two of the biggest industries in harmony. Fintech disruption is a deviation from the norm, resulting in a significant shift in banking services and, as a result, risk. This article aims to investigate how Fintech has influenced recent changes in the banking industry and upcoming challenges, with a particular emphasis on blockchain technology. We perform a comprehensive thematic analysis of recent studies on Fintech in the banking industry. We found that Fintech has enormous potential to grow and impact the banking industry and the entire world. The banking industry could benefit from combining emerging technologies such as blockchain, AI, machine learning, or other decision-making layers. However, with the benefits come drawbacks, such as increased reliance on technology, high costs, increased job losses, security risks related to data and fraud, and so on. The use of emerging technology and collaboration between Fintech firms and banks can improve system-wide financial stability while minimising the negative externalities of disruption and competition. These findings can help regulators, policymakers, academics, and practitioners understand the opportunities and challenges of emerging technologies in the banking industry. Full article

This research inquiry analyzed the association between corporate governance and firm performance through the mediating role of financial leverage based on panel data of 2568 firms during the period from 2002 to 2017. The study uses a two-step dynamic panel as well as a generalized method of moments (GMM) to estimate these relationships. The findings demonstrated financial leverage mediates the relationship between corporate governance and firm performance in the context of developed economies, and also in emerging economies. Additionally, firm performance is negatively associated with corporate governance through excessive leverage. The study suggests it is the responsibility of the board to use low financial leverage to enhance firm performance. In emerging countries, firms with a large-sized board use low leverage, whereas in developed countries, firms with a small-sized board use low leverage to enhance corporate performance. Full article

In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the credit-scoring context. The model list includes the binormal, bigamma, bibeta, bilogistic, power, and bifractal curves. The models are then tested against empirical credit-scoring ROC data from publicly available presentations and papers, as well as from European retail lending institutions. Except for the power curve, all the presented models fit the data quite well. However, based on the results and other favourable properties, it is suggested that the binormal curve is the preferred choice for modelling credit-scoring ROC curves. Full article