Risks

26 pages, 1446 KiB

Open AccessEditor’s ChoiceArticle

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 3 | Viewed by 3338

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 KiB

Open AccessEditor’s ChoiceArticle

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 5 | Viewed by 4057

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 KiB

Open AccessEditor’s ChoiceArticle

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 12 | Viewed by 23700

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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19 pages, 1774 KiB

Open AccessEditor’s ChoiceArticle

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 2 | Viewed by 1763

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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32 pages, 6252 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 2283

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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18 pages, 925 KiB

Open AccessEditor’s ChoiceArticle

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 9 | Viewed by 9756

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 KiB

Open AccessEditor’s ChoiceArticle

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 3 | Viewed by 3019

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 KiB

Open AccessEditor’s ChoiceArticle

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 3 | Viewed by 3872

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 KiB

Open AccessEditor’s ChoiceArticle

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 2199

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 KiB

Open AccessEditor’s ChoiceArticle

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 1582

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 KiB

Open AccessEditor’s ChoiceArticle

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 2380

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 KiB

Open AccessEditor’s ChoiceArticle

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 3468

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 1665

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 KiB

Open AccessEditor’s ChoiceArticle

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 1635

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 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 4113

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 KiB

Open AccessEditor’s ChoiceArticle

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 1632

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 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 1567

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 KiB

Open AccessEditor’s ChoiceArticle

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 2263

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 4 | Viewed by 2724

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 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 4533

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 KiB

Open AccessEditor’s ChoiceArticle

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 2436

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 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 1909

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

(This article belongs to the Special Issue Advancement in Mortality Forecasting and Mortality/Longevity Risk Management)

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23 pages, 8949 KiB

Open AccessEditor’s ChoiceArticle

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

Viewed by 3730

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 2414

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

(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)

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26 pages, 769 KiB

Open AccessEditor’s ChoiceArticle

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 2763

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 3161

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 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 2260

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 KiB

Open AccessEditor’s ChoiceArticle

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 2579

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 4 | Viewed by 2699

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 2630

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 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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 3274

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 KiB

Open AccessEditor’s ChoiceArticle

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 10 | Viewed by 10206

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 KiB

Open AccessEditor’s ChoiceArticle

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 10 | Viewed by 11265

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

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Non-life Insurance: Theory, Methods and Applications)

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30 pages, 7109 KiB

Open AccessEditor’s ChoiceReview

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 5593

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 KiB

Open AccessEditor’s ChoiceArticle

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 21 | Viewed by 25745

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 KiB

Open AccessEditor’s ChoiceArticle

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 26 | Viewed by 20686

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 KiB

Open AccessEditor’s ChoiceArticle

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 17 | Viewed by 12446

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 KiB

Open AccessEditor’s ChoiceArticle

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 10 | Viewed by 3952

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 KiB

Open AccessEditor’s ChoiceArticle

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 1 | Viewed by 7301

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

(This article belongs to the Special Issue Emerging Topics in Finance and Risk Engineering—In Memory of Peter Carr)

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18 pages, 1156 KiB

Open AccessEditor’s ChoiceArticle

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 3133

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 KiB

Open AccessEditor’s ChoiceArticle

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 24 | Viewed by 27541

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 KiB

Open AccessEditor’s ChoiceArticle

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 2187

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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14 pages, 462 KiB

Open AccessEditor’s ChoiceReview

Cryptocurrencies as Gamblified Financial Assets and Cryptocasinos: Novel Risks for a Public Health Approach to Gambling

by Maira Andrade and Philip W. S. Newall

Risks 2023, 11(3), 49; https://doi.org/10.3390/risks11030049 - 22 Feb 2023

Cited by 15 | Viewed by 6096

Abstract

Policymakers’ attempts to prevent gambling-related harm are affected by the ‘gamblification’ of, for example, video games and investing. This review highlights related issues posed by cryptocurrencies, which are decentralised and volatile digital assets, and which underlie ‘cryptocasinos’—a new generation of online gambling operators. [...] Read more.

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

29 pages, 1138 KiB

Open AccessEditor’s ChoiceArticle

Optimal Investment in a Dual Risk Model

by Arash Fahim and Lingjiong Zhu

Risks 2023, 11(2), 41; https://doi.org/10.3390/risks11020041 - 9 Feb 2023

Cited by 3 | Viewed by 2672

Abstract

Dual risk models are popular for modeling a venture capital or high-tech company, for which the running cost is deterministic and the profits arrive stochastically over time. Most of the existing literature on dual risk models concentrates on the optimal dividend strategies. In [...] Read more.

Dual risk models are popular for modeling a venture capital or high-tech company, for which the running cost is deterministic and the profits arrive stochastically over time. Most of the existing literature on dual risk models concentrates on the optimal dividend strategies. In this paper, we propose to study the optimal investment strategy on research and development for the dual risk models to minimize the ruin probability of the underlying company. We will also study the optimization problem when, in addition, the investment in a risky asset is allowed. Full article

(This article belongs to the Special Issue Emerging Topics in Finance and Risk Engineering—In Memory of Peter Carr)

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25 pages, 708 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Dependence Modelling of Lifetimes in Egyptian Families

by Kira Henshaw, Waleed Hana, Corina Constantinescu and Dalia Khalil

Risks 2023, 11(1), 18; https://doi.org/10.3390/risks11010018 - 11 Jan 2023

Cited by 1 | Viewed by 2945

Abstract

In this study, we analyse a large sample of Egyptian social pension data which covers, by law, the policyholder’s spouse, children, parents and siblings. This data set uniquely enables the study and comparison of pairwise dependence between multiple familial relationships beyond the well-known [...] Read more.

In this study, we analyse a large sample of Egyptian social pension data which covers, by law, the policyholder’s spouse, children, parents and siblings. This data set uniquely enables the study and comparison of pairwise dependence between multiple familial relationships beyond the well-known husband and wife case. Applying Bayesian Markov Chain Monte Carlo (MCMC) estimation techniques with the two-step inference functions for margins (IFM) method, we model dependence between lifetimes in spousal, parent–child and child–parent relationships, using copulas to capture the strength of association. Dependence is observed to be strongest in child–parent relationships and, in comparison to the high-income countries of data sets previously studied, of lesser significance in the husband and wife case, often referred to as broken-heart syndrome. Given the traditional use of UK mortality tables in the modelling of mortality in Egypt, the findings of this paper will help to inform appropriate mortality assumptions specific to the unique structure of the Egyptian scheme. Full article

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21 pages, 2093 KiB

Open AccessEditor’s ChoiceArticle

A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO₂ Emissions

by Nini Johana Marín-Rodríguez, Juan David González-Ruiz and Sergio Botero

Risks 2023, 11(1), 15; https://doi.org/10.3390/risks11010015 - 9 Jan 2023

Cited by 30 | Viewed by 5473

Abstract

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO₂ emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility [...] Read more.

Wavelet power spectrum (WPS) and wavelet coherence analyses (WCA) are used to examine the co-movements among oil prices, green bonds, and CO₂ emissions on daily data from January 2014 to October 2022. The WPS results show that oil returns exhibit significant volatility at low and medium frequencies, particularly in 2014, 2019–2020, and 2022. Also, the Green Bond Index presents significant volatility at the end of 2019–2020 and the beginning of 2022 at low, medium, and high frequencies. Additionally, CO₂ futures’ returns present high volatility at low and medium frequencies, expressly in 2015–2016, 2018, the end of 2019–2020, and 2022. WCA’s empirical findings reveal (i) that oil returns have a negative impact on the Green Bond Index in the medium term. (ii) There is a strong interdependence between oil prices and CO₂ futures’ returns, in short, medium, and long terms, as inferred from the time–frequency analysis. (iii) There also is evidence of strong short, medium, and long terms co-movements between the Green Bond Index and CO₂ futures’ returns, with the Green Bond Index leading. Full article

(This article belongs to the Special Issue Data Analysis and Financial Risk Management in Financial Markets)

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14 pages, 1224 KiB

Open AccessEditor’s ChoiceArticle

Risk Measures in Simulation-Based Business Valuation: Classification of Risk Measures in Risk Axiom Systems and Application in Valuation Practice

by Dietmar Ernst

Risks 2023, 11(1), 13; https://doi.org/10.3390/risks11010013 - 6 Jan 2023

Cited by 2 | Viewed by 3096

Abstract

Simulation-based company valuations are based on an analysis of the risks in the company to be valued. This means that risk analysis is decisively important in a simulation-based business valuation. The link between risk measures, risk conception and risk axiom systems has not [...] Read more.

Simulation-based company valuations are based on an analysis of the risks in the company to be valued. This means that risk analysis is decisively important in a simulation-based business valuation. The link between risk measures, risk conception and risk axiom systems has not yet been sufficiently elaborated for simulation-based business valuations. The aim of this study was to determine which understanding of risk underlies simulation-based business valuations and how this can be implemented via suitable risk measures in simulation-based business valuations. The contribution of this study is providing guidance for the methodologically correct selection of appropriate risk measures. This will help with avoiding valuation errors. To this end, the findings were combined from risk axiom systems with the valuation equations of simulation-based business valuations. Only position-invariant risk measures are suitable for simulation-based business valuations. Full article

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13 pages, 347 KiB

Open AccessEditor’s ChoiceArticle

Regulating Robo-Advisors in Insurance Distribution: Lessons from the Insurance Distribution Directive and the AI Act

by Pierpaolo Marano and Shu Li

Risks 2023, 11(1), 12; https://doi.org/10.3390/risks11010012 - 4 Jan 2023

Cited by 10 | Viewed by 4519

Abstract

Insurance distributors are increasingly using robo-advisors for a variety of tasks, ranging from facilitating communication with customers to providing substantive advice. Like many other AI-empowered applications, robo-advisors have the potential to pose substantial risks that should be regulated and corrected by legal instruments. [...] Read more.

Insurance distributors are increasingly using robo-advisors for a variety of tasks, ranging from facilitating communication with customers to providing substantive advice. Like many other AI-empowered applications, robo-advisors have the potential to pose substantial risks that should be regulated and corrected by legal instruments. In this article, we attempt to discuss the regulation of robo-advisors from the perspective of the Insurance Distribution Directive and the draft AI Act. We ask two questions for each. (1) From a positive legal perspective, what obligations are imposed on insurance distributors by the legislation when they deploy robo-advisors in their business? (2) From a normative perspective, are the incumbent provisions within that legislation effective at ensuring the ethical and responsible use of robo-advisors? Our results show that neither the Insurance Distribution Directive nor the AI Act adequately address the emerging risks associated with robo-advisors. The rules implicated by them regarding the use of robo-advisors for insurance distribution are inconsistent, disproportionate, and implicit. Legislators shall further address these issues, and authorities such as EIOPA and national competent authorities must also participate by providing concrete guidelines. Full article

(This article belongs to the Special Issue The Risk Landscape in the Digital Transformation of Finance and Insurance)

16 pages, 1207 KiB

Open AccessEditor’s ChoiceArticle

Development of the PRISM Risk Assessment Method Based on a Multiple AHP-TOPSIS Approach

by Ferenc Bognár, Balázs Szentes and Petra Benedek

Risks 2022, 10(11), 213; https://doi.org/10.3390/risks10110213 - 9 Nov 2022

Cited by 16 | Viewed by 4339

Abstract

The PRISM method is a risk assessment approach that focuses on hidden-risk identification and ranking. The combined AHP-PRISM method was created for strategic assessments based on pairwise comparisons. The PRISM and AHP-PRISM methods have remarkable visual decision support and control functions that make [...] Read more.

The PRISM method is a risk assessment approach that focuses on hidden-risk identification and ranking. The combined AHP-PRISM method was created for strategic assessments based on pairwise comparisons. The PRISM and AHP-PRISM methods have remarkable visual decision support and control functions that make them useful in practical problem solving. However, the methods can be successfully applied with the same factor weights. To eliminate this significant disadvantage and enable an in-depth analysis of the alternatives based on the ideal best and ideal worst solutions, AHP-PRISM was integrated with TOPSIS in this study. As a result, the novel AHP-TOPSIS-based PRISM method can be configured more extensively for practical decision-making problems than the previous PRISM approaches. In addition, the novel method supports the ideal best and worst analysis of the alternatives without losing its ability to focus on identifying hidden risk. The method was tested on data related to strategic incident groups of incoming logistics business processes at a nuclear power plant. Full article

(This article belongs to the Special Issue New Advance of Risk Management Models)

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18 pages, 3377 KiB

Open AccessEditor’s ChoiceReview

A Systematic Literature Review of Volatility and Risk Management on Cryptocurrency Investment: A Methodological Point of View

by José Almeida and Tiago Cruz Gonçalves

Risks 2022, 10(5), 107; https://doi.org/10.3390/risks10050107 - 19 May 2022

Cited by 37 | Viewed by 15714

Abstract

In this study, we explore the research published from 2009 to 2021 and summarize what extant literature has contributed in the last decade to the analysis of volatility and risk management in cryptocurrency investment. Our samples include papers published in journals ranked across [...] Read more.

In this study, we explore the research published from 2009 to 2021 and summarize what extant literature has contributed in the last decade to the analysis of volatility and risk management in cryptocurrency investment. Our samples include papers published in journals ranked across different fields in ABS ranked journals. We conduct a bibliometric analysis using VOSviewer software and perform a literature review. Our findings are presented in terms of methodologies used to model cryptocurrencies’ volatility and also according to their main findings pertaining to volatility and risk management in those assets and using them in portfolio management. Our research indicates that the models that consider the Markov-switching regime seem to be more consensual among the authors, and that the best machine learning technique performances are hybrid models that consider the support vector machines (SVM). We also argue that the predictability of volatility, risk reduction, and level of speculation in the cryptocurrency market are improved by the leverage effects and the volatility persistence. Full article

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