Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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28 pages, 491 KB  
Article
Enterprise Risk Management, Financial Reporting and Firm Operations
by Siwei Gao, Hsiao-Tang Hsu and Fang-Chun Liu
Risks 2025, 13(3), 48; https://doi.org/10.3390/risks13030048 - 3 Mar 2025
Cited by 2 | Viewed by 2679
Abstract
We examine financial reporting and firm operations, focusing specifically on the roles of ‘enterprise risk management’ (ERM), within which a holistic approach is taken to the conceptualization and management of all types of risk. We measure ERM implementation based on information obtained from [...] Read more.
We examine financial reporting and firm operations, focusing specifically on the roles of ‘enterprise risk management’ (ERM), within which a holistic approach is taken to the conceptualization and management of all types of risk. We measure ERM implementation based on information obtained from 2004–2014 financial reports on 648 firms. We find that ERM implementation is associated with higher reporting quality and reduced volatility in future firm performance in terms of both operating cash flows and stock returns. Our difference-in-differences analyses indicate that these associations were strengthened by the introduction of the Securities and Exchange Commission (SEC) final rule in 2010, requiring increased and improved disclosure related to risk oversight. Our findings, which we attribute to the incremental effects of ERM and enhanced risk disclosure over time, point to the substantial advantages of ERM and the importance of related disclosure, which should prove to be of interest to firms as well as policymakers. Full article
27 pages, 10976 KB  
Article
Cyber, Geopolitical, and Financial Risks in Rare Earth Markets: Drivers of Market Volatility
by Emilia Calefariu Giol, Oana Panazan and Catalin Gheorghe
Risks 2025, 13(3), 46; https://doi.org/10.3390/risks13030046 - 28 Feb 2025
Cited by 2 | Viewed by 2192
Abstract
This study examines the integrated impacts of cyberattacks, geopolitical, and financial market volatility on rare earth markets during the 2014–2024 period, using Time-Varying Parameter Vector Autoregression and wavelet analysis. By bridging critical gaps in the literature, this research provides a comprehensive framework for [...] Read more.
This study examines the integrated impacts of cyberattacks, geopolitical, and financial market volatility on rare earth markets during the 2014–2024 period, using Time-Varying Parameter Vector Autoregression and wavelet analysis. By bridging critical gaps in the literature, this research provides a comprehensive framework for understanding the compounded effects of emerging risks on market dynamics. The analysis includes key market indices (SOLLIT, PICK, SPGSIN, GSPTXGM, MVREMXTR, and XME), alongside green energy prices, to capture cross-market dependencies. The findings reveal that financial volatility exerts the most persistent long-term influence, while geopolitical events, such as the US-China trade tensions and the Ukraine conflict, trigger significant market disruptions. Cyberattacks, although episodic, exacerbate short-term volatility, especially during global crises. Rising green energy prices further amplify vulnerabilities in supply chains, underscoring the interconnectedness of rare earth markets and the sustainable energy transition. This research provides actionable insights for integrated risk management strategies, emphasizing supply chain diversification, enhanced cybersecurity, and international cooperation to ensure market stability and resilience in the energy transition. Full article
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19 pages, 941 KB  
Article
ESG and Financial Distress: The Role of Bribery, Corruption, and Fraud in FTSE All-Share Companies
by Probowo Erawan Sastroredjo and Tarsisius Renald Suganda
Risks 2025, 13(3), 41; https://doi.org/10.3390/risks13030041 - 24 Feb 2025
Cited by 3 | Viewed by 2083
Abstract
Our investigation examined the impact of ESG (Environmental, Social, and Governance) activities on corporate financial distress. This research utilised data from companies listed in the FTSE All-Share index from 2014 to 2022 from the Refinitiv EIKON database. We incorporated year- and industry-fixed effects [...] Read more.
Our investigation examined the impact of ESG (Environmental, Social, and Governance) activities on corporate financial distress. This research utilised data from companies listed in the FTSE All-Share index from 2014 to 2022 from the Refinitiv EIKON database. We incorporated year- and industry-fixed effects into our analysis to address changing economic conditions and industry-specific effects. ESG scores were used as a proxy for ESG activities, while Z-scores were utilised to gauge financial distress. The results unveiled a compelling trend: ESG activities showcased a negative correlation with financial distress, implying that companies actively involved in ESG actions are less likely to face default, even after incorporating several robustness and endogeneity tests. Moreover, when examining the role of bribery, corruption, and fraud issues (negative issues) as a moderating factor, our findings revealed that lower negative issues strengthen the negative relationship between ESG (governance pillar) and financial distress. This suggests that governance mechanisms effectively reduce financial distress in less corrupt environments, where institutional quality supports properly implementing governance practices. These findings offer valuable insights for companies seeking to mitigate financial distress by adopting ESG strategies. Full article
(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)
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23 pages, 2121 KB  
Article
Evaluating Transition Rules for Enhancing Fairness in Bonus–Malus Systems: An Application to the Saudi Arabian Auto Insurance Market
by Asrar Alyafie, Corina Constantinescu and Jorge Yslas
Risks 2025, 13(1), 18; https://doi.org/10.3390/risks13010018 - 20 Jan 2025
Viewed by 1435
Abstract
A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition [...] Read more.
A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition rules dictating the movements of policyholders within the system, and the relativities used to determine premium adjustments. This paper explores the impact of modifications to these three elements on risk classification, assessed through the mean squared error. The model parameters are calibrated with real-world data from the Saudi auto insurance market. We begin the analysis by focusing on transition rules based solely on claim frequency, a framework in which most implemented BMSs work, including the current Saudi BMS. We then consider transition rules that depend on frequency and severity, in which higher penalties are given for large claim sizes. The results show that increasing the number of levels typically improves risk segmentation but requires balancing practical implementation constraints and that the adequate selection of the penalties is critical to enhancing fairness. Moreover, the study reveals that incorporating a severity-based penalty enhances risk differentiation, especially when there is a dependence between the claim frequency and severity. Full article
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25 pages, 5146 KB  
Article
Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model
by Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai and Qi Cao
Risks 2025, 13(1), 17; https://doi.org/10.3390/risks13010017 - 17 Jan 2025
Cited by 1 | Viewed by 8018
Abstract
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) [...] Read more.
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization. Full article
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18 pages, 4380 KB  
Article
Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market
by Abhinav Das and Stephan Schlüter
Risks 2025, 13(1), 13; https://doi.org/10.3390/risks13010013 - 16 Jan 2025
Viewed by 1606
Abstract
In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, [...] Read more.
In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, this work introduces a risk management strategy based on a combination of the well-known risk measures of the Value at Risk (VaR) and Conditional Value at Risk (CVaR). The goal is to optimize electricity procurement by forecasting hourly prices over a certain horizon and allocating a fixed budget using the aforementioned measures to minimize the financial risk. To generate price predictions, a Gaussian process regression model is used. The aim of this hybrid approach is to design a model that is easily understandable but allows for a comprehensive evaluation of potential financial exposure. It enables consumers to adjust their consumption patterns or market traders to invest and allows more cost-effective and risk-aware decision-making. The potential of our approach is shown in a case study based on the German market. Moreover, by discussing the political and economical implications, we show how the implementation of our method can contribute to the realization of a sustainable, flexible, and efficient energy market, as outlined in Germany’s Renewable Energy Act. Full article
(This article belongs to the Special Issue Financial Derivatives and Hedging in Energy Markets)
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21 pages, 4282 KB  
Article
Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums
by Gonzalo Cortazar, Hector Ortega and José Antonio Pérez
Risks 2025, 13(1), 9; https://doi.org/10.3390/risks13010009 - 10 Jan 2025
Cited by 1 | Viewed by 2459
Abstract
This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with [...] Read more.
This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures markets and analysts’ forecasts. Risk premiums are computed by comparing expected and futures prices. The model considers that risk premiums are not solely determined by contract maturity but also by the marketing crop years. These crop years, in turn, are influenced by the respective harvest periods, a crucial factor in the agricultural commodity market. Results show that risk premiums vary across commodities, with some exhibiting positive and others negative values. While maturity affects risk premiums’ size, sign, and shape, the crop year plays a critical role, especially in the case of wheat. As speculators in the financial markets demand a positive risk premium, its sign provides insights into whether they are buyers or sellers of futures for each crop year, maturity, and commodity. This research offers valuable insights into grain price behavior, highlighting their similarities and differences. These findings have significant practical implications for market participants seeking to refine their trading and risk management strategies and for future research on the industry structure for each crop. Moreover, this enhanced understanding of risk premiums can be directly applied in the finance and agricultural industries, improving decision-making processes. Full article
(This article belongs to the Special Issue Financial Derivatives and Their Applications)
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23 pages, 423 KB  
Article
The Impact of Hyperbolic Discounting on Asset Accumulation for Later Life: A Study of Active Investors Aged 65 Years and over in Japan
by Honoka Nabeshima, Sumeet Lal, Haruka Izumi, Yuzuha Himeno, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya
Risks 2025, 13(1), 8; https://doi.org/10.3390/risks13010008 - 5 Jan 2025
Cited by 2 | Viewed by 2732
Abstract
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 [...] Read more.
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. Full article
26 pages, 1446 KB  
Article
riskAIchain: AI-Driven IT Infrastructure—Blockchain-Backed Approach for Enhanced Risk Management
by Mir Mehedi Rahman, Bishwo Prakash Pokharel, Sayed Abu Sayeed, Sujan Kumar Bhowmik, Naresh Kshetri and Nafiz Eashrak
Risks 2024, 12(12), 206; https://doi.org/10.3390/risks12120206 - 19 Dec 2024
Cited by 5 | Viewed by 4354
Abstract
In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain [...] Read more.
In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments. By leveraging artificial intelligence for predictive analytics, anomaly detection, and data-driven decision-making, combined with blockchain’s secure and immutable ledger for data integrity and transparency, the proposed infrastructure offers a robust solution to existing challenges in risk management. The infrastructure is adaptable and scalable to support a variety of risk management methodologies, providing a more secure, efficient, and intelligent system. The findings highlight significant improvements in the accuracy, speed, and reliability of risk management, underscoring the infrastructure’s capability to proactively address emerging cyber threats. To ensure the proposed model effectively addresses the most critical issues, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique will be used to analyze and evaluate the interrelationships among the existing critical factors. This approach evaluates the interrelationships and impacts of these factors, verifying the model’s comprehensiveness in managing organizational risk. This study lays the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity. Full article
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15 pages, 430 KB  
Article
The Role of Green Credit in Bank Profitability and Stability: A Case Study on Green Banking in Indonesia
by Sutrisno Sutrisno, Agus Widarjono and Abdul Hakim
Risks 2024, 12(12), 198; https://doi.org/10.3390/risks12120198 - 10 Dec 2024
Cited by 6 | Viewed by 6737
Abstract
Green credits are one of the alternative bank loans to the traditional sector. In addition, this green credit supports sustainability and environmental issues. This paper analyzes the influence of green credits on bank profits and stability in Indonesia. This study analyzed banks in [...] Read more.
Green credits are one of the alternative bank loans to the traditional sector. In addition, this green credit supports sustainability and environmental issues. This paper analyzes the influence of green credits on bank profits and stability in Indonesia. This study analyzed banks in Indonesia that provided green credits. Of 140 banks, only 35 banks disbursed green credits starting in 2019. Our study examined all banks providing green credit from 2019 to 2022 using annual data. The results of the study showed that green credits have a positive effect on profits, but green credits have no effect on bank stability. Small banks benefit from green credits in encouraging profitability. In addition, the profitability and stability of banks in Indonesia are greatly influenced by strong bank fundamentals such as capital and efficiency. This study has important implications in both theoretical and practical aspects. Because green credit supports profitability, the bank must diversify the loans in both the traditional sector as well as new sectors that are related to environmental issues and development sustainability following the theory of loan diversification. For practical implication, the Indonesian Financial Service Authority as a policymaker requires each bank to provide financing related to green credits. Full article
33 pages, 9119 KB  
Article
Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers
by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra and Jiabin Luo
Risks 2024, 12(11), 174; https://doi.org/10.3390/risks12110174 - 4 Nov 2024
Cited by 15 | Viewed by 31665
Abstract
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify [...] Read more.
The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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32 pages, 6252 KB  
Article
News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks
by Hamed Mirashk, Amir Albadvi, Mehrdad Kargari and Mohammad Ali Rastegar
Risks 2024, 12(11), 171; https://doi.org/10.3390/risks12110171 - 30 Oct 2024
Cited by 2 | Viewed by 3048
Abstract
This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, [...] Read more.
This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts. Full article
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19 pages, 1774 KB  
Article
Effective Machine Learning Techniques for Dealing with Poor Credit Data
by Dumisani Selby Nkambule, Bhekisipho Twala and Jan Harm Christiaan Pretorius
Risks 2024, 12(11), 172; https://doi.org/10.3390/risks12110172 - 30 Oct 2024
Cited by 3 | Viewed by 2261
Abstract
Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit [...] Read more.
Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level. Full article
(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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18 pages, 925 KB  
Article
Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence
by M. K. Nallakaruppan, Himakshi Chaturvedi, Veena Grover, Balamurugan Balusamy, Praveen Jaraut, Jitendra Bahadur, V. P. Meena and Ibrahim A. Hameed
Risks 2024, 12(10), 164; https://doi.org/10.3390/risks12100164 - 15 Oct 2024
Cited by 13 | Viewed by 13343
Abstract
The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are [...] Read more.
The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates. Full article
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25 pages, 2301 KB  
Article
Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints
by Hossein Ghanbari, Emran Mohammadi, Amir Mohammad Larni Fooeik, Ronald Ravinesh Kumar, Peter Josef Stauvermann and Mostafa Shabani
Risks 2024, 12(10), 163; https://doi.org/10.3390/risks12100163 - 11 Oct 2024
Cited by 6 | Viewed by 4245
Abstract
The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these [...] Read more.
The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape. Full article
(This article belongs to the Special Issue Cryptocurrency Pricing and Trading)
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16 pages, 408 KB  
Article
Behavioral Biases in Panic Selling: Exploring the Role of Framing during the COVID-19 Market Crisis
by Yu Kuramoto, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya
Risks 2024, 12(10), 162; https://doi.org/10.3390/risks12100162 - 10 Oct 2024
Cited by 4 | Viewed by 4901
Abstract
Panic selling causes long-term losses and hinders investors’ return to the market. It has been explained using prospect theory aspects such as loss and regret aversion. Additionally, overconfidence and overreaction contribute to the disposition effect, leading investors to sell stocks prematurely. However, the [...] Read more.
Panic selling causes long-term losses and hinders investors’ return to the market. It has been explained using prospect theory aspects such as loss and regret aversion. Additionally, overconfidence and overreaction contribute to the disposition effect, leading investors to sell stocks prematurely. However, the framing effect, another disposition effect attribute, has been underexplored in the context of panic selling. This study investigates how the framing effect influences panic selling, particularly during market crises, when investors perceive information differently, depending on its positive or negative framing. Utilizing data from a collaborative survey, we examine Japanese investors’ behavior during the COVID-19 market crisis. Negative framing is negatively associated with complete or partial sale of securities, whereas positive framing has the opposite effect. During market crises, investors presented with negative framing are less likely to panic sell, whereas those presented with positive framing are more prone to it. Other significant factors include gender; men tend to engage more in panic selling. Conversely, higher education, financial literacy, and greater household income and assets are associated with a reduced likelihood of panic selling. These findings underscore the critical role of framing in investor behavior during market crises, providing new insights into the mechanisms underlying panic selling. Full article
33 pages, 5094 KB  
Article
Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation
by Farha Usman, Jennifer S. K. Chan, Udi E. Makov, Yang Wang and Alice X. D. Dong
Risks 2024, 12(9), 137; https://doi.org/10.3390/risks12090137 - 28 Aug 2024
Viewed by 3029
Abstract
We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that [...] Read more.
We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies. Full article
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12 pages, 948 KB  
Article
Fair and Sustainable Pension System: Market Equilibrium Using Implied Options
by Ishay Wolf and Lorena Caridad López del Río
Risks 2024, 12(8), 127; https://doi.org/10.3390/risks12080127 - 8 Aug 2024
Cited by 2 | Viewed by 1782
Abstract
This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests [...] Read more.
This study contributes to the discussion about a fair and balanced pension system with a collectively funded pension scheme or social security and a defined contribution pillar. With an invigorated risk approach using financial option positions, it considers the variance of socioeconomic interests of different society-earning cohorts. By that, it enables the assumption of un-uniformity in interests about the fair and sustainable pension design. Specifically, we claim that the alternative cost of hedging the ideal position to the counterparty position studies the implied risks and returns that participants are willing to absorb and hence may lead to a fair compromise when there are different interests. The novelty of the introduced method is mainly based on the variety of participants’ risks and not on the utility function. Accordingly, we spare the discussion about the right shape of the utility function and the proper calibrations. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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27 pages, 1930 KB  
Article
Determinants of the Effectiveness of Risk Management in the Project Portfolio in the FinTech Industry
by Oliwia Khalil-Oliwa and Izabela Jonek-Kowalska
Risks 2024, 12(7), 111; https://doi.org/10.3390/risks12070111 - 4 Jul 2024
Cited by 1 | Viewed by 3042
Abstract
Risk management in the project portfolio can contribute to more effective implementation of the goals of the projects, the portfolio, and the entire organization. However, in the literature on the subject, relatively little attention is paid to the determinants of this process. Moreover, [...] Read more.
Risk management in the project portfolio can contribute to more effective implementation of the goals of the projects, the portfolio, and the entire organization. However, in the literature on the subject, relatively little attention is paid to the determinants of this process. Moreover, the process course is rarely analyzed in a strategic context relating to the entire organization. For these reasons, this article’s primary goal is to identify the determinants of the effectiveness of risk management in the project portfolio. Research in this area was carried out in the FinTech industry, and the results were analyzed using structural equation modeling. The results indicated that the most important dimensions of the examined effectiveness are the strategic orientation of the organization and the risk management process in the project portfolio. At the level of strategic orientation, this highlights the need for coherence between the organization’s strategy and the project portfolio. At the level of risk management in the project portfolio, the primacy of ownership and control of individual risks is clearly visible. Full article
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16 pages, 649 KB  
Article
The Complementary Nature of Financial Risk Aversion and Financial Risk Tolerance
by John Grable, Abed Rabbani and Wookjae Heo
Risks 2024, 12(7), 109; https://doi.org/10.3390/risks12070109 - 2 Jul 2024
Cited by 2 | Viewed by 4616
Abstract
Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk [...] Read more.
Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk tolerance (an investor’s willingness to engage in a behavior based on their subjective evaluation of the uncertainty of the outcomes) are inversely-related substitutes. The purpose of this paper is to present an alternative way of viewing these constructs. We show that risk aversion and risk tolerance act as complementary factors in models designed to describe the degree of risk observed in household investment portfolios. A series of multivariate tests were used to determine that financial risk aversion is inversely related to portfolio risk, whereas financial risk tolerance is positively associated with portfolio risk. When used in the same model, the amount of explained variance in portfolio risk was increased compared to models where one, but not the other, measure was used. Overall, financial risk tolerance exhibited the largest model effect, although financial risk aversion was also important across the models analyzed in this study. Full article
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17 pages, 1070 KB  
Article
Can Multi-Peril Insurance Policies Mitigate Adverse Selection?
by Peter Zweifel and Annette Hofmann
Risks 2024, 12(6), 102; https://doi.org/10.3390/risks12060102 - 20 Jun 2024
Viewed by 1788
Abstract
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the [...] Read more.
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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17 pages, 496 KB  
Article
Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses
by Tianxing Yan, Yi Lu and Himchan Jeong
Risks 2024, 12(6), 97; https://doi.org/10.3390/risks12060097 - 16 Jun 2024
Viewed by 1954
Abstract
The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to [...] Read more.
The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total. Full article
(This article belongs to the Special Issue Statistical Modelling in Risk Management)
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17 pages, 1062 KB  
Article
Deep Learning Option Price Movement
by Weiguan Wang and Jia Xu
Risks 2024, 12(6), 93; https://doi.org/10.3390/risks12060093 - 4 Jun 2024
Cited by 1 | Viewed by 5605
Abstract
Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider [...] Read more.
Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features. Full article
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10 pages, 2477 KB  
Article
Multi-Timescale Recurrent Neural Networks Beat Rough Volatility for Intraday Volatility Prediction
by Damien Challet and Vincent Ragel
Risks 2024, 12(6), 84; https://doi.org/10.3390/risks12060084 - 22 May 2024
Viewed by 1832
Abstract
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory [...] Read more.
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series. Full article
(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
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16 pages, 725 KB  
Article
Cyber Risk in Insurance: A Quantum Modeling
by Claude Lefèvre, Muhsin Tamturk, Sergey Utev and Marco Carenzo
Risks 2024, 12(5), 83; https://doi.org/10.3390/risks12050083 - 20 May 2024
Cited by 1 | Viewed by 1820
Abstract
In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to [...] Read more.
In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Risk Theory)
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19 pages, 512 KB  
Article
Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm
by Robin Van Oirbeek, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau and Tim Verdonck
Risks 2024, 12(5), 79; https://doi.org/10.3390/risks12050079 - 14 May 2024
Cited by 1 | Viewed by 2484
Abstract
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where [...] Read more.
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector. Full article
(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
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26 pages, 882 KB  
Article
Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact
by Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo and Jacopo Maria Ricci
Risks 2024, 12(5), 78; https://doi.org/10.3390/risks12050078 - 11 May 2024
Cited by 5 | Viewed by 3329
Abstract
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and [...] Read more.
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks. Full article
(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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21 pages, 1481 KB  
Article
Determining Safe Withdrawal Rates for Post-Retirement via a Ruin-Theory Approach
by Diba Daraei and Kristina Sendova
Risks 2024, 12(4), 70; https://doi.org/10.3390/risks12040070 - 19 Apr 2024
Cited by 2 | Viewed by 7388
Abstract
To ensure a comfortable post-retirement life and the ability to cover living expenses, it is of utmost importance for individuals to have a clear understanding of how long their pre-retirement savings will last. In this research, we employ a ruin-theory approach to model [...] Read more.
To ensure a comfortable post-retirement life and the ability to cover living expenses, it is of utmost importance for individuals to have a clear understanding of how long their pre-retirement savings will last. In this research, we employ a ruin-theory approach to model the inflows and the outflows of retirees’ portfolios. We track all transactions within the portfolios of retired clients sourced by a registered investment provider to Canada’s Financial Wellness Lab at Western University. By utilizing an advanced ruin model, we calculate the mean and the median time it takes for savings to be exhausted, the probabilities of exhaustion of funds within the retirees’ expected remaining lifetime while accounting for the observed withdrawal rates, and the deficit at ruin if a retiree has used up all of their savings. We also account for gender as well as for the risk tolerance of retired clients using a K-Means clustering algorithm. This allows us to compare the financial outcomes for female and male retirees and to enhance some findings in the literature. In the final phase of our study, we compare the results obtained by our methodology to the 4% rule which is a widely used approach for post-retirement spending. Our results show that most retirees can withdraw safely more than they currently do (around 2.5%). A withdrawal rate of about 4.5% is proved to be safe, but it might not provide sufficient income for most retirees since it yields approximately CAD 20,000 per year for male retirees in the highest risk tolerance group who withdraw about 4.5% annually. Full article
(This article belongs to the Special Issue Optimal Investment and Risk Management)
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31 pages, 1109 KB  
Article
Relationship between Occupational Pension, Corporate Social Responsibility (CSR), and Organizational Resilience: A Study on Listed Chinese Companies
by Hao Wang, Tao Zhang, Xi Wang and Jiansong Zheng
Risks 2024, 12(4), 65; https://doi.org/10.3390/risks12040065 - 9 Apr 2024
Viewed by 2655
Abstract
Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social [...] Read more.
Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social responsibility (CSR) and organizational resilience. Drawing on insights from cost-stickiness and resource-based theories, we developed a model that elucidated the influence of occupational pensions on firms’ approaches to CSR within the context of COVID-19 and how this, in turn, impacted organizational resilience. This study categorized CSR into strategic and responsive activities, employing the concept of cost stickiness as a framework. We analyzed a sample of 34,145 observations from Chinese A-share listed companies spanning the period 2010–2023 to examine the influence of occupational pension adjustments on CSR strategies. The findings of this study revealed that the cost pressure associated with contributions to occupational pensions prompted firms to decrease their engagement in responsive CSR activities while enhancing their strategic CSR initiatives. Furthermore, it was observed that strategic CSR contributed to improved organizational resilience, whereas responsive CSR did not exhibit the same effect. The relationship between occupational pension contributions and CSR was found to be significantly and negatively moderated by factors such as the minimum wage and population aging. Conversely, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities. Full article
(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)
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17 pages, 2884 KB  
Article
Adding Shocks to a Prospective Mortality Model
by Frédéric Planchet and Guillaume Gautier de La Plaine
Risks 2024, 12(3), 57; https://doi.org/10.3390/risks12030057 - 20 Mar 2024
Cited by 1 | Viewed by 2066
Abstract
This work proposes a simple model to take into account the annual volatility of the mortality level observed on the scale of a country like France in the construction of prospective mortality tables. By assigning a frailty factor to a basic hazard function, [...] Read more.
This work proposes a simple model to take into account the annual volatility of the mortality level observed on the scale of a country like France in the construction of prospective mortality tables. By assigning a frailty factor to a basic hazard function, we generalise the Lee–Carter model. The impact on prospective life expectancies and capital requirements in the context of a life annuity scheme is analysed in detail. Full article
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23 pages, 8949 KB  
Article
Climate Change-Related Disaster Risk Mitigation through Innovative Insurance Mechanism: A System Dynamics Model Application for a Case Study in Latvia
by Maksims Feofilovs, Andrea Jonathan Pagano, Emanuele Vannucci, Marina Spiotta and Francesco Romagnoli
Risks 2024, 12(3), 43; https://doi.org/10.3390/risks12030043 - 28 Feb 2024
Cited by 2 | Viewed by 4062
Abstract
This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster [...] Read more.
This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model’s structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model’s plausibility. The case study’s findings support the innovative insurance mechanism’s dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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29 pages, 610 KB  
Article
Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data
by Claudio Mazzi, Angelo Damone, Andrea Vandelli, Gastone Ciuti and Milena Vainieri
Risks 2024, 12(2), 24; https://doi.org/10.3390/risks12020024 - 29 Jan 2024
Cited by 2 | Viewed by 2608
Abstract
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods [...] Read more.
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management. Full article
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26 pages, 769 KB  
Article
Maximum Pseudo-Likelihood Estimation of Copula Models and Moments of Order Statistics
by Alexandra Dias
Risks 2024, 12(1), 15; https://doi.org/10.3390/risks12010015 - 18 Jan 2024
Cited by 1 | Viewed by 3082
Abstract
It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance [...] Read more.
It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance applications when data are scarce. We show that the canonical MPL method uses the mean of order statistics, and we propose to use the median or the mode instead. We show that the MPL estimators proposed are consistent and asymptotically normal. In a simulation study, we compare the finite sample performance of the proposed estimators with that of the original MPL and the inversion method estimators based on Kendall’s tau and Spearman’s rho. In our results, the modified MPL estimators, especially the one based on the mode of the order statistics, have a better finite sample performance both in terms of bias and mean square error. An application to general insurance data shows that the level of dependence estimated between different products can vary substantially with the estimation method used. Full article
(This article belongs to the Special Issue Interplay between Financial and Actuarial Mathematics II)
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24 pages, 659 KB  
Article
Option Pricing and Portfolio Optimization under a Multi-Asset Jump-Diffusion Model with Systemic Risk
by Roman N. Makarov
Risks 2023, 11(12), 217; https://doi.org/10.3390/risks11120217 - 13 Dec 2023
Cited by 1 | Viewed by 3500
Abstract
We explore a multi-asset jump-diffusion pricing model, combining a systemic risk asset with several conditionally independent ordinary assets. Our approach allows for analyzing and modeling a portfolio that integrates high-activity security, such as an exchange trading fund (ETF) tracking a major market index [...] Read more.
We explore a multi-asset jump-diffusion pricing model, combining a systemic risk asset with several conditionally independent ordinary assets. Our approach allows for analyzing and modeling a portfolio that integrates high-activity security, such as an exchange trading fund (ETF) tracking a major market index (e.g., S&P500), along with several low-activity securities infrequently traded on financial markets. The model retains tractability even as the number of securities increases. The proposed framework allows for constructing models with common and asset-specific jumps with normally or exponentially distributed sizes. One of the main features of the model is the possibility of estimating parameters for each asset price process individually. We present the conditional maximum likelihood estimation (MLE) method for fitting asset price processes to empirical data. For the case with common jumps only, we derive a closed-form solution to the conditional MLE method for ordinary assets that works even if the data are incomplete and asynchronous. Alternatively, to find risk-neutral parameters, the least-square method calibrates the model to option values. The number of parameters grows linearly in the number of assets compared to the quadratic growth through the correlation matrix, which is typical for many other multi-asset models. We delve into the properties of the proposed model, its parameter estimation using the MLE method and least-squares technique, the evaluation of VaR and CVaR metrics, the identification of optimal portfolios, and the pricing of European-style basket options. We propose a Laplace-transform-based approach to computing Value at Risk (VaR) and conditional VaR (also known as the expected shortfall) of portfolio returns. Additionally, European-style basket options written on the extreme and average stock prices or returns can be evaluated semi-analytically. For numerical demonstration, we examine a combination of the SPDR S&P 500 ETF (as a systemic risk asset) with eight ordinary assets representing diverse industries. Using historical assets and options prices, we estimate the real-world and risk-neutral parameters of the model with common jumps, construct several optimal portfolios, and evaluate various basket options with the eight assets. The results affirm the robustness and efficiency of the estimation and evaluation methodologies. Computational results are compared with Monte Carlo estimates. Full article
(This article belongs to the Special Issue Optimal Investment and Risk Management)
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18 pages, 537 KB  
Article
On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach
by Shuai Yang and Kenneth Q. Zhou
Risks 2023, 11(12), 206; https://doi.org/10.3390/risks11120206 - 27 Nov 2023
Cited by 2 | Viewed by 2548
Abstract
In the insurance industry, life insurers are required by regulators to meet capital requirements to avoid insolvency caused by, for example, sudden mortality changes due to the COVID-19 pandemic. To prevent any large movements in this required capital, insurance companies are motivated to [...] Read more.
In the insurance industry, life insurers are required by regulators to meet capital requirements to avoid insolvency caused by, for example, sudden mortality changes due to the COVID-19 pandemic. To prevent any large movements in this required capital, insurance companies are motivated to establish hedging strategies to mitigate the inherent risk exposures they face. Nonetheless, devising and implementing risk mitigation solutions to risk managing capital requirement is frequently impeded by the computational complexities stemming from the extensive simulations required. In this paper, we delve into a simulation quandary concerning the management of solvency capital risk associated with mortality and longevity. More specifically, we introduce a thin-plate regression spline method as a surrogate alternative to the standard nested simulation approach. Using this efficient simulation method, we further investigate hedging strategies that utilize mortality-linked securities coupled with stochastic mortality dynamics. Our simulation results provide a numerical justification to the market-making of mortality-linked securities in the context of mortality and longevity capital risk management. Full article
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16 pages, 2692 KB  
Article
Claims Modelling with Three-Component Composite Models
by Jackie Li and Jia Liu
Risks 2023, 11(11), 196; https://doi.org/10.3390/risks11110196 - 13 Nov 2023
Cited by 2 | Viewed by 2815
Abstract
In this paper, we develop a number of new composite models for modelling individual claims in general insurance. All our models contain a Weibull distribution for the smallest claims, a lognormal distribution for the medium-sized claims, and a long-tailed distribution for the largest [...] Read more.
In this paper, we develop a number of new composite models for modelling individual claims in general insurance. All our models contain a Weibull distribution for the smallest claims, a lognormal distribution for the medium-sized claims, and a long-tailed distribution for the largest claims. They provide a more detailed categorisation of claims sizes when compared to the existing composite models which differentiate only between the small and large claims. For each proposed model, we express four of the parameters as functions of the other parameters. We fit these models to two real-world insurance data sets using both maximum likelihood and Bayesian estimation, and test their goodness-of-fit based on several statistical criteria. They generally outperform the existing composite models in the literature, which comprise only two components. We also perform regression using the proposed models. Full article
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21 pages, 1107 KB  
Article
New Classes of Distortion Risk Measures and Their Estimation
by Jungsywan H. Sepanski and Xiwen Wang
Risks 2023, 11(11), 194; https://doi.org/10.3390/risks11110194 - 10 Nov 2023
Cited by 5 | Viewed by 3043
Abstract
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of [...] Read more.
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of an existing non-negative random variable whose distribution function, named the generating distribution, may contain more than one parameter. The coherency of the resulting risk measures is ensured by restricting the parameter space on which the distortion function is concave. We studied cases when the generating distributions are exponentiated exponential and Gompertz distributions. Closed-form expressions for risk measures were derived for uniform, exponential, and Lomax losses. Numerical and graphical results are presented to examine the effects of the parameter values on the risk measures. We then propose a simple plug-in estimate of risk measures and conduct simulation studies to compare and demonstrate the performance of the proposed estimates. The plug-in estimates appear to perform slightly better than the well-known L-estimates, but also suffer from biases when applied to heavy-tailed losses. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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37 pages, 582 KB  
Article
Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves
by Anas Abdallah and Lan Wang
Risks 2023, 11(11), 187; https://doi.org/10.3390/risks11110187 - 26 Oct 2023
Cited by 2 | Viewed by 2762
Abstract
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the [...] Read more.
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family of multivariate distributions to the actuarial literature. In fact, we study rank-based methods using the Sarmanov distribution to adequately estimate the loss reserves and properly capture the dependence between lines of business. An inadequate choice of the dependence structure may negatively impact the estimation of the marginals and, hence, the reserve. Thus, we propose a two-stage inference strategy in this research to address this, while taking advantage of the flexibility of the Sarmanov distribution. We show that this strategy leads to a more robust estimation, and better captures the dependence between the risks. We also show that it generates smaller risk capital and a better diversification benefit. We extend the model to the multivariate case with more than two lines of business. To illustrate and validate our methods, we use three different sets of real data from both a major US property–casualty insurer and a large Canadian insurance company. Full article
(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)
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25 pages, 857 KB  
Article
Assessing the Impact of Credit Risk on Equity Options via Information Contents and Compound Options
by Federico Maglione and Maria Elvira Mancino
Risks 2023, 11(10), 183; https://doi.org/10.3390/risks11100183 - 20 Oct 2023
Viewed by 3687
Abstract
This work aims to develop a measure of how much credit risk is priced into equity options. Such a measure appears particularly appealing when applied to a portfolio of equity options, as it allows for the factoring in of firm-specific default dynamics, thus [...] Read more.
This work aims to develop a measure of how much credit risk is priced into equity options. Such a measure appears particularly appealing when applied to a portfolio of equity options, as it allows for the factoring in of firm-specific default dynamics, thus producing a comparable statistic across different equities. As a matter of fact, comparing options written on different equities based on their moneyness does offer much guidance in understanding which option offers a better hedging against default. Our newly-introduced measure aims to fulfil this gap: it allows us to rank options written on different names based on the amount of default risk they carry, incorporating firm-specific characteristics such as leverage and asset risk. After having computed this measure using data from the US market, several empirical tests confirm the economic intuition of puts being more sensitive to changes in the default risk as well as a good integration of the CDS and option markets. We further document cross-sectional sectorial differences based on the industry the companies operate in. Moreover, we show that this newly-introduced measure displays forecasting power in explaining future changes in the skew of long-term maturity options. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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18 pages, 757 KB  
Article
An Analysis of Volatility and Risk-Adjusted Returns of ESG Indices in Developed and Emerging Economies
by Hemendra Gupta and Rashmi Chaudhary
Risks 2023, 11(10), 182; https://doi.org/10.3390/risks11100182 - 19 Oct 2023
Cited by 14 | Viewed by 11588
Abstract
The importance of Environmental, Social, and Governance (ESG) aspects in investment decisions has grown significantly in today’s volatile financial market. This study aims to answer the important question of whether investing in ESG-compliant companies is a better option for investors in both developed [...] Read more.
The importance of Environmental, Social, and Governance (ESG) aspects in investment decisions has grown significantly in today’s volatile financial market. This study aims to answer the important question of whether investing in ESG-compliant companies is a better option for investors in both developed and emerging markets. This study assesses ESG investment performance in diverse regions, focusing on developed markets with high GDP, specifically the USA, Germany, and Japan, alongside emerging nations, India, Brazil, and China. We compare ESG indices against respective broad market indices, all comprising large and mid-cap stocks. This study employs a variety of risk-adjusted criteria to systematically compare the performance of ESG indices against broad market indices. The evaluation also delves into downside volatility, a crucial factor for portfolio growth. It also explores how news events impact ESG and market indices in developed and emerging economies using the EGARCH model. The findings show that, daily, there is no significant difference in returns between ESG and conventional indices. However, when assessing one-year rolling returns, ESG indices outperform the overall market indices in all countries except Brazil, exhibiting positive alpha and offering better risk-adjusted returns. ESG portfolios also provide more downside risk protection, with higher upside beta than downside beta in most countries (except the USA and India). Furthermore, negative news has a milder impact on the volatility of ESG indices in all of the studied countries except for Germany. This suggests that designing a portfolio based on ESG-compliant companies could be a prudent choice for investors, as it yields relatively better risk-adjusted returns compared to the respective market indices. Furthermore, there is insufficient evidence to definitively establish that the performance of ESG indices varies significantly between developed and emerging markets. Full article
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20 pages, 1128 KB  
Article
Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting
by Carina Clemente, Gracinda R. Guerreiro and Jorge M. Bravo
Risks 2023, 11(9), 163; https://doi.org/10.3390/risks11090163 - 12 Sep 2023
Cited by 13 | Viewed by 13685
Abstract
Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, [...] Read more.
Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance. Full article
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30 pages, 7109 KB  
Review
Overview of Some Recent Results of Energy Market Modeling and Clean Energy Vision in Canada
by Anatoliy Swishchuk
Risks 2023, 11(8), 150; https://doi.org/10.3390/risks11080150 - 14 Aug 2023
Viewed by 5782
Abstract
This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts [...] Read more.
This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts modeling with delayed and jumped volatilities, applications of mean-reverting processes on Alberta energy markets, and alternatives to the Black-76 model for options valuation of futures contracts. We will also consider the clean renewable energy prospective in Canada, and, in particular, in Alberta and Calgary. Full article
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32 pages, 2302 KB  
Article
Do Behavioral Biases Affect Investors’ Investment Decision Making? Evidence from the Pakistani Equity Market
by Zain UI Abideen, Zeeshan Ahmed, Huan Qiu and Yiwei Zhao
Risks 2023, 11(6), 109; https://doi.org/10.3390/risks11060109 - 6 Jun 2023
Cited by 24 | Viewed by 29119
Abstract
Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play [...] Read more.
Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play in the decision making process. We first document the empirical evidence to support that the behavioral biases and market anomalies are closely associated and that these two factors significantly influence the investors’ investment decision making. The additional analyses confirm the mediating roles of certain market anomalies in the association between the investors’ behavioral biases and their investment decision making. Furthermore, empirical evidence reveals that financial literacy moderates the association between behavioral biases and market anomalies, and eventually influences the investors’ investment decision making. Overall, although the results are inconclusive for the relationships between certain variables, our results highlight the importance of financial literacy in terms of optimal investment decision making of individuals and the stability of the overall stock market. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)
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17 pages, 491 KB  
Article
The Relationship between Capital Structure and Firm Performance: The Moderating Role of Agency Cost
by Amanj Mohamed Ahmed, Deni Pandu Nugraha and István Hágen
Risks 2023, 11(6), 102; https://doi.org/10.3390/risks11060102 - 1 Jun 2023
Cited by 30 | Viewed by 23220
Abstract
Since it first appeared, agency theory has argued that debt can decrease agency issues between agent and principal and enhance the value of firms. This paper explores the moderating effect of agency cost on the association between capital structure and firm performance. A [...] Read more.
Since it first appeared, agency theory has argued that debt can decrease agency issues between agent and principal and enhance the value of firms. This paper explores the moderating effect of agency cost on the association between capital structure and firm performance. A panel econometric method, namely a fixed-effect regression model, was used to evaluate the above description. This investigation uses secondary data collected from published annual reports of manufacturing firms listed on Tehran Stock Exchange (TSE) during 2011–2019. Empirical results show that capital structure is negatively related to firm performance. Agency cost also has a negative impact on corporate performance; however, in the case of ROA and EPS, the relationship is positive. Interestingly, the findings illustrate that increasing the level of debt can reduce agency costs and enhance firm performance. Moreover, robust correlations are revealing that agency cost significantly affects the relationship between capital structure and corporate performance. These findings provide proof to support the assumptions of agency theory, which explains the association between capital structure and performance of firms. This study provides new perspectives on the relationship between capital structure and firm performance by using data from listed manufacturing firms in Iran; hence, these new insights from a developing market improve the understanding of capital structure in Asian and Middle Eastern markets. Full article
22 pages, 4519 KB  
Article
Context-Based and Adaptive Cybersecurity Risk Management Framework
by Henock Mulugeta Melaku
Risks 2023, 11(6), 101; https://doi.org/10.3390/risks11060101 - 31 May 2023
Cited by 19 | Viewed by 13989
Abstract
Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing [...] Read more.
Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing their common problems regarding cybersecurity culture, developing a cyber-risk management plan, and devising a more proactive and collaborative approach that is suitable according to their organization context. To this end, firstly various enterprise, Information Technology (IT), and cybersecurity risk management frameworks are thoroughly reviewed along with their advantages and limitations. Then, we propose a proactive cybersecurity risk management framework that is simple and dynamic, and that adapts according to the current threat and technology landscapes and organizational context. Finally, performance metrics to evaluate the framework are proposed. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2023)
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33 pages, 1019 KB  
Article
Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector
by Rasa Kanapickienė, Tomas Kanapickas and Audrius Nečiūnas
Risks 2023, 11(5), 97; https://doi.org/10.3390/risks11050097 - 18 May 2023
Cited by 12 | Viewed by 4452
Abstract
Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in [...] Read more.
Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk. Full article
(This article belongs to the Special Issue Credit Risk Management: Volume II)
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18 pages, 794 KB  
Article
A Diversification Framework for Multiple Pairs Trading Strategies
by Kiseop Lee, Tim Leung and Boming Ning
Risks 2023, 11(5), 93; https://doi.org/10.3390/risks11050093 - 16 May 2023
Cited by 2 | Viewed by 8783
Abstract
We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows [...] Read more.
We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework. Full article
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18 pages, 1156 KB  
Article
Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models
by Jan Reig Torra, Montserrat Guillen, Ana M. Pérez-Marín, Lorena Rey Gámez and Giselle Aguer
Risks 2023, 11(3), 57; https://doi.org/10.3390/risks11030057 - 9 Mar 2023
Cited by 7 | Viewed by 3375
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Risks: Feature Papers 2023)
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15 pages, 406 KB  
Article
Cryptocurrency Risks, Fraud Cases, and Financial Performance
by David S. Kerr, Karen A. Loveland, Katherine Taken Smith and Lawrence Murphy Smith
Risks 2023, 11(3), 51; https://doi.org/10.3390/risks11030051 - 23 Feb 2023
Cited by 25 | Viewed by 33512
Abstract
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 [...] Read more.
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 article belongs to the Special Issue Cryptocurrencies and Risk Management)
11 pages, 693 KB  
Article
Measuring Systemic Governmental Reinsurance Risks of Extreme Risk Events
by Elroi Hadad, Tomer Shushi and Rami Yosef
Risks 2023, 11(3), 50; https://doi.org/10.3390/risks11030050 - 23 Feb 2023
Cited by 1 | Viewed by 2358
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Catastrophe Risk and Insurance)
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