This paper presents an integrated risk management methodology for measuring and managing the economics, risks, and financial resources/constraints related to deposits and loans in a commercial bank. Within a comprehensive and integrated framework, we develop valuation and risk models for all financial products on the bank’s balance sheet. Our proposed methodology aligns with regulatory requirements while offering a practical implementation. Unlike traditional industry practices, which often rely on fragmented and siloed risk management solutions, our approach integrates risk modeling across all aspects of the bank’s balance sheet. This new perspective provides a more accurate and consistent assessment of financial risks, improving the bank’s ability to navigate regulatory and economic challenges.

The study aims to examine the enterprise risk management (ERM) practices of Ethiopian commercial banks. This approach is undertaken to examine the current approach to enterprise risk management within the Ethiopian banking context. A mixed-methods research design is employed which comprises content analysis and a survey study. The study found that the prevailing emphasis of risk management functions in Ethiopian commercial banks revolves on ensuring compliance with regulatory reporting standards. A significant number of the banks have implemented ERM programs primarily to meet regulatory obligations, rather than leveraging ERM to generate firm value. The study identified several gaps in the risk management function of Ethiopian commercial banks, including lack of integration of risk management with the banks’ mission and core values, failure to assess the resources required for effective risk management and to prioritise resource allocation accordingly, inadequate coverage of relevant activities and functional areas by both risk management and internal audit activities, and limitations on the assignment of chief risk officers (CROs) to oversee the risk management function within the banks. Overall, the maturity level of ERM implementation among Ethiopian commercial banks is moderate and requires further enhancement.

This article examines the relationship between implementing sustainable development measures and financial risk in the context of global companies and the recycling economy. This study uses statistics from Forbes, TIME, and Statista on 50 global companies that actively embrace sustainable development and recycling-economy practices across various industries. As a result, we have compiled a Structural Equation Model (SEM), with the help of which we established that growth in the activity of their implementing the measures of sustainable development and the recycling economy by each 1 point leads to a reduction in the risk of a shortfall in global companies’ profit by USD 0.0741 billion and the risk of ousting global companies from the market by USD 1.8374 billion. It has also been revealed that a reduction in the risk of the shortfall in profit by each USD 1 billion is accompanied by an increase in the activity of global companies’ implementing the measures of sustainable development and the recycling economy by 0.3433 points, and a reduction in the risk of market displacement by each USD 1 billion is accompanied by a growth in this activity by 0.0073 points. The theoretical novelty of the research consists of substantiating the differences in the consequences of the development of the recycling economy for financial risks of companies from different sectors. Practical implications of the proposed recommendations for companies in different industries are that the authors’ recommendations for the development of the recycling economy will allow for systemic reduction in financial risks in the sectors “Automotive Industry & Suppliers”, “Banking, Insurance & Financial Services”, “Chemicals, Drugs & Biotechnology”, and “Retail, Wholesale & Consumer Goods”. We have also revealed the threat of growth of all financial risks in the course of the development of the recycling economy in the sphere “Transportation, Logistics & Aviation”. In “Electronics, Hardware & Equipment” and “Manufacturing & Industrial Production”, the implications are differentiated among financial risks, which require flexibility and care during the development of the recycling economy. We find that global companies’ implementation of sustainable development measures, recycling economy practices, and financial risks are mutually dependent organizational phenomena. Moreover, the risk to profits and market displacement manifest differently among global industries. Our conclusions support expediency in implementing sustainable development and recycling-economy measures to reduce the financial risks to global companies. Further, we propose practical recommendations for companies from different sectors of the world economy.

This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions.

In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a single scoring approach may bias the loan evaluation conclusion. In this paper, we propose a bivariate model based on the integration of two scoring approaches. We first formulate the loan evaluation task as a multi-target problem, in which loan_status (i.e., default or not default) is used as the discrete outcome for the credit risk measure while the annualized rate of return (ARR) is used as the continuous outcome for the profitability measure. Then to solve the multi-target problem, we design a novel loss function based on the assumption that the discrete outcome follows a Bernoulli distribution, and the continuous outcome is normally distributed conditional on the discrete output. The effectiveness of the proposed model is examined using the real-world P2P data from the Lending Club. Results indicate that our approach outperforms the sole scoring methods by identifying loans with higher profit and lower default risk. Therefore, the proposed method can serve as an alternative for loan evaluation.

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry.

Asset accumulation in later life is a pressing issue in Japan due to the growing gap between life expectancy (87.14 years for women, 81.09 years for men in 2023) and the retirement age (65 or less). This gap heightens financial insecurity, emphasizing the need to meet asset goals by 65. Hyperbolic discounting, driven by present-biased preferences, often hinders this process, but empirical evidence for those aged 65 and older remains limited. Moreover, prior research has overlooked the varying impacts of hyperbolic discounting across different wealth levels. This study addresses these gaps by analyzing data from 6709 active Japanese investors aged over 65 (2023 wave) using probit regression. Wealth thresholds are categorized into four levels: JPY 20 million, JPY 30 million, JPY 50 million, and JPY 100 million. The results show that hyperbolic discounting significantly impairs asset accumulation at the JPY 100 million level but not at lower thresholds. This effect likely reflects the complex nature of hyperbolic discounting, which primarily affects long-term savings and investments. The findings underscore the importance of addressing hyperbolic discounting in later-life financial planning. Recommendations include implementing automatic savings plans, enhancing financial literacy, and incorporating behavioral insights into planning tools to support better asset accumulation outcomes.

In this paper, we provide empirical evidence of the market price of risk for delivery periods (MPDP) of electricity swap contracts. The MPDP enables an accurate pricing of such contracts in the presence of the delivery period such that the typical approximations can be avoided. In our empirical study, we focus on term-structure effects and identify the resulting MPDP. In presence of the Samuelson effect, we find the most pronounced MPDP close to maturity, while the MPDP disappears proportional to the Samuelson effect far away from maturity. Thus, our theory improves the pricing accuracy close to maturity.

This research aims to examine the drivers of earnings quality (EQ) in emerging stock markets. By testing the impact of firm attributes and ownership structures on the level of earnings quality. The research followed a mixed-method approach (qualitative and quantitative) and was conducted based on a sample of 75 listed firms in Egypt as an emerging market from 2015 to 2022. The results of the research found that each firm attribute has a mixed impact on earning quality, such as firm size (positive on persistence and no impact on consistency) and ROA (U-shape on persistence and consistency). In addition, ownership structures uniquely and dynamically impact earnings, following positive, U-shape, and N-shape. This research sheds light on the drivers of the earnings quality (firm attributes and ownership structures) in the Egyptian-listed firms and helps policymakers implement appropriate corporate governance mechanisms. Our findings in Egypt can motivate future research to further investigate the most relevant factors that explain variations in earning persistence and consistency as a dimension of financial reporting quality in other emerging markets.

We introduce the Quantum Alarm System, a novel framework that combines the informational advantages of quantum majorization applied to tail pseudo-correlation matrices with the learning capabilities of a reinforced urn process, to predict financial turmoil and market crashes. This integration allows for a more nuanced analysis of the dependence structure in financial markets, particularly focusing on extreme events reflected in the tails of the distribution. Our model is tested using the daily log-returns of the 30 constituents of the Dow Jones Industrial Average, spanning from 2 January 1992 to 30 August 2024. The results are encouraging: in the validation set, the 12-month ahead probability of correct alarm is between 73% and 80%, while maintaining a low false alarm rate. Thanks to the application of quantum majorization, the alarm system effectively captures non-traditional and emerging risk sources, such as the financial impact of the COVID-19 pandemic—an area where traditional models often fall short.