Journal Description
Risks
Risks
is an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management. Risks is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: JCR - Q2 (Business, Finance) / CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.7 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done
Impact Factor:
2.0 (2023);
5-Year Impact Factor:
1.7 (2023)
Latest Articles
The Role of Entrepreneur’s Face Disclosure on Crowdfunding Success
Risks 2024, 12(10), 165; https://doi.org/10.3390/risks12100165 (registering DOI) - 15 Oct 2024
Abstract
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The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data
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The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data were collected from multiple crowdfunding platforms for projects in Africa. That is, cross-country data from 54 African countries, to overcome data limitations from a single country. An econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success by 3%. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges to the success of online crowdfunding.
Full article
Open AccessArticle
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 (registering DOI) - 15 Oct 2024
Abstract
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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
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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
Figure 1
Figure 1
<p>Analysis of the papers surveyed and the technologies discussed.</p> Full article ">Figure 2
<p>Credit risk evaluation architecture.</p> Full article ">Figure 3
<p>Confusion matrix for the decision tree classifier.</p> Full article ">Figure 4
<p>Confusion matrix for the random forest classifier.</p> Full article ">Figure 5
<p>Credit risk explanation with local surrogates.</p> Full article ">Figure 6
<p>Summary plot for feature important versus output magnitude.</p> Full article ">Figure 7
<p>Force plot explanation for the distribution of data.</p> Full article ">Figure 8
<p>Dependency plot in global explanation.</p> Full article ">Figure 9
<p>Decision plot in global explanation for low risk.</p> Full article ">
<p>Analysis of the papers surveyed and the technologies discussed.</p> Full article ">Figure 2
<p>Credit risk evaluation architecture.</p> Full article ">Figure 3
<p>Confusion matrix for the decision tree classifier.</p> Full article ">Figure 4
<p>Confusion matrix for the random forest classifier.</p> Full article ">Figure 5
<p>Credit risk explanation with local surrogates.</p> Full article ">Figure 6
<p>Summary plot for feature important versus output magnitude.</p> Full article ">Figure 7
<p>Force plot explanation for the distribution of data.</p> Full article ">Figure 8
<p>Dependency plot in global explanation.</p> Full article ">Figure 9
<p>Decision plot in global explanation for low risk.</p> Full article ">
Open AccessArticle
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
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
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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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<p>A triangular fuzzy number.</p> Full article ">Figure 2
<p>A trapezoid fuzzy number.</p> Full article ">Figure 3
<p>Credibility of triangular fuzzy variable.</p> Full article ">Figure 4
<p>Credibility of trapezoidal fuzzy variable.</p> Full article ">Figure 5
<p>The presentation of portfolios under different scenarios. Source: Authors’ own estimation.</p> Full article ">Figure 5 Cont.
<p>The presentation of portfolios under different scenarios. Source: Authors’ own estimation.</p> Full article ">
<p>A triangular fuzzy number.</p> Full article ">Figure 2
<p>A trapezoid fuzzy number.</p> Full article ">Figure 3
<p>Credibility of triangular fuzzy variable.</p> Full article ">Figure 4
<p>Credibility of trapezoidal fuzzy variable.</p> Full article ">Figure 5
<p>The presentation of portfolios under different scenarios. Source: Authors’ own estimation.</p> Full article ">Figure 5 Cont.
<p>The presentation of portfolios under different scenarios. Source: Authors’ own estimation.</p> Full article ">
Open AccessArticle
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
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
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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
Open AccessArticle
Operating Cost Flexibility and Implications for Stock Returns
by
Roi D. Taussig
Risks 2024, 12(10), 161; https://doi.org/10.3390/risks12100161 - 10 Oct 2024
Abstract
This study suggests a new measure for a firm’s operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a
[...] Read more.
This study suggests a new measure for a firm’s operating cost flexibility. Flexible firms are less risky and, therefore, require lower stock returns. This analysis of 126,202 firm-year observations from the U.S. cross-section of stock returns finds that the new measure explains a negative significant rate of return. The new measure’s impact extends beyond that of operating leverage. In addition, the new measure’s impact is both statistically and economically significant, and it is sustainable for a variety of in-sample and out-of-sample robustness tests. The new findings are beneficial to researchers and practitioners alike.
Full article
Open AccessArticle
Risk Retention and Management Implications of Medical Malpractice in the Italian Health Service
by
Ilaria Colivicchi, Tommaso Fabbri and Antonio Iannizzotto
Risks 2024, 12(10), 160; https://doi.org/10.3390/risks12100160 - 8 Oct 2024
Abstract
This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the
[...] Read more.
This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the industry landscape has been characterised by periods of distress for this type of protection, with rising litigations and reimbursement costs, resulting in a peculiarly complex market. For the Italian context, the study focuses on the financial repercussions for healthcare institutions of the growing trend towards risk retention practises, legally empowered by the introduction of Law No. 24/2017. The analysis employs Generalised Linear Models for the regressive approach to incorporate the structural and organisational characteristics of hospitals and uses quantitative simulations to explore different scenarios at a regional aggregate level. Due to the limited existing literature and data on the topic, this research aims to provide new methods for effectively understanding and managing this type of risk, thereby supporting decision-making processes in the healthcare sector.
Full article
(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)
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Figure 1
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<p>Loss distribution in terms of Expected and Unexpected Losses. The figure illustrates the distribution of losses for a given risk exposure, highlighting key components used in Value at Risk analysis. The horizontal axis represents the possible loss amounts, while the vertical axis represents the probability density of those losses. Source: <a href="#B2-risks-12-00160" class="html-bibr">Basel Committee on Banking Supervision</a> (<a href="#B2-risks-12-00160" class="html-bibr">2005</a>).</p> Full article ">Figure 2
<p>Distribution of public healthcare facilities among regions (2021). The figure illustrates the distribution of public healthcare facilities across various regions for the year 2021. The blue bars indicate the number of hospitals in each Region. Source: <a href="#B9-risks-12-00160" class="html-bibr">Direzione Generale della Digitalizzazione del Sistema Informativo Sanitario e della Statistica, Ufficio di Statistica</a> (<a href="#B9-risks-12-00160" class="html-bibr">2021</a>).</p> Full article ">Figure 3
<p>Clustering results using Ward’s hierarchical-agglomerative method. (<b>a</b>) The figure presents the visualisation of the clusters (“A”, “B”, “C”, and “D”) in a two-dimensional space. This visualisation is achieved by applying Principal Component Analysis (PCA) to reduce the dimensionality of the original data. (<b>b</b>) The histogram depicts the distribution of data points across clusters. Each blue bar corresponds to one of the clusters and indicates the number of data points assigned to each cluster. Source: own elaboration.</p> Full article ">Figure 4
<p>The optimal choice for the number of clusters. (<b>a</b>) The figure illustrates the dendrogram used to assess hierarchical clustering. A dashed line indicates the cut-off point for determining the number of clusters. Each cluster is distinguished by a unique color, with branches of the same color merging into a single group. This visualizes how observations with similar characteristics are grouped together at various stages of the clustering process. (<b>b</b>) The figure displays a plot of the Silhouette Scores, which are used to evaluate the quality of clustering. The plot demonstrates how the Silhouette Score varies with the number of clusters. The peak indicates the optimal number of clusters. Source: own elaboration.</p> Full article ">Figure 5
<p>“Geographical Area” variable’s distribution. The horizontal bar chart represents the number of healthcare organisations located in different geographical areas of Italy. The y-axis labels the geographical areas (“North”, “Centre”, “South”), while the x-axis represents the count of healthcare organisations in each area. Source: own elaboration.</p> Full article ">Figure 6
<p>Estimated frequencies and severities. (<b>a</b>) The bar chart presents the estimated frequencies of the 502 healthcare organisations considered in the analysis. (<b>b</b>) The combined chart shows both the estimated severities (red line) and observed severities (dark blue bars) for the same set of healthcare facilities. Source: own elaboration.</p> Full article ">Figure 7
<p>Simulation procedure. The figure illustrates the simulation procedure, starting on the left with the generation of 1000 random numbers representing claims frequencies. These random numbers are then linked to their corresponding claims amount, resulting in a vector of severity values. Finally, data are aggregated to produce a vector of 1000 observations. Source: own elaboration.</p> Full article ">Figure 8
<p>Claims probability distributions on an aggregate level for some regions. Histograms show the probability distributions of aggregated claims for various regions, respectively, Lombardy, Lazio, Valle d’Aosta, and Basilicata. Sky blue bars represent the distribution of aggregated claims, overlaid by dashed lines indicating key statistical metrics: a red dashed line for the mean, a green dashed line for the 75th percentile VaR, a blue dashed line for the 80th percentile VaR, and a yellow dashed line for the 85th percentile VaR. Source: own elaboration.</p> Full article ">
<p>Loss distribution in terms of Expected and Unexpected Losses. The figure illustrates the distribution of losses for a given risk exposure, highlighting key components used in Value at Risk analysis. The horizontal axis represents the possible loss amounts, while the vertical axis represents the probability density of those losses. Source: <a href="#B2-risks-12-00160" class="html-bibr">Basel Committee on Banking Supervision</a> (<a href="#B2-risks-12-00160" class="html-bibr">2005</a>).</p> Full article ">Figure 2
<p>Distribution of public healthcare facilities among regions (2021). The figure illustrates the distribution of public healthcare facilities across various regions for the year 2021. The blue bars indicate the number of hospitals in each Region. Source: <a href="#B9-risks-12-00160" class="html-bibr">Direzione Generale della Digitalizzazione del Sistema Informativo Sanitario e della Statistica, Ufficio di Statistica</a> (<a href="#B9-risks-12-00160" class="html-bibr">2021</a>).</p> Full article ">Figure 3
<p>Clustering results using Ward’s hierarchical-agglomerative method. (<b>a</b>) The figure presents the visualisation of the clusters (“A”, “B”, “C”, and “D”) in a two-dimensional space. This visualisation is achieved by applying Principal Component Analysis (PCA) to reduce the dimensionality of the original data. (<b>b</b>) The histogram depicts the distribution of data points across clusters. Each blue bar corresponds to one of the clusters and indicates the number of data points assigned to each cluster. Source: own elaboration.</p> Full article ">Figure 4
<p>The optimal choice for the number of clusters. (<b>a</b>) The figure illustrates the dendrogram used to assess hierarchical clustering. A dashed line indicates the cut-off point for determining the number of clusters. Each cluster is distinguished by a unique color, with branches of the same color merging into a single group. This visualizes how observations with similar characteristics are grouped together at various stages of the clustering process. (<b>b</b>) The figure displays a plot of the Silhouette Scores, which are used to evaluate the quality of clustering. The plot demonstrates how the Silhouette Score varies with the number of clusters. The peak indicates the optimal number of clusters. Source: own elaboration.</p> Full article ">Figure 5
<p>“Geographical Area” variable’s distribution. The horizontal bar chart represents the number of healthcare organisations located in different geographical areas of Italy. The y-axis labels the geographical areas (“North”, “Centre”, “South”), while the x-axis represents the count of healthcare organisations in each area. Source: own elaboration.</p> Full article ">Figure 6
<p>Estimated frequencies and severities. (<b>a</b>) The bar chart presents the estimated frequencies of the 502 healthcare organisations considered in the analysis. (<b>b</b>) The combined chart shows both the estimated severities (red line) and observed severities (dark blue bars) for the same set of healthcare facilities. Source: own elaboration.</p> Full article ">Figure 7
<p>Simulation procedure. The figure illustrates the simulation procedure, starting on the left with the generation of 1000 random numbers representing claims frequencies. These random numbers are then linked to their corresponding claims amount, resulting in a vector of severity values. Finally, data are aggregated to produce a vector of 1000 observations. Source: own elaboration.</p> Full article ">Figure 8
<p>Claims probability distributions on an aggregate level for some regions. Histograms show the probability distributions of aggregated claims for various regions, respectively, Lombardy, Lazio, Valle d’Aosta, and Basilicata. Sky blue bars represent the distribution of aggregated claims, overlaid by dashed lines indicating key statistical metrics: a red dashed line for the mean, a green dashed line for the 75th percentile VaR, a blue dashed line for the 80th percentile VaR, and a yellow dashed line for the 85th percentile VaR. Source: own elaboration.</p> Full article ">
Open AccessArticle
Why Do Companies Share Buybacks? Evidence from the UK
by
Yasmin Jamadar, Hossain Mohammad Reyad, Md. Kausar Alam, Oli Ahad Thakur and Syed A. Mamun
Risks 2024, 12(10), 159; https://doi.org/10.3390/risks12100159 - 8 Oct 2024
Abstract
We examine the key drivers behind management decisions on share repurchase from various theoretical perspectives, including the free cash flow theory and the signaling theory/hypothesis. Specifically, we investigate the relationship between share repurchase and three key drivers: surplus cash, undervaluation, and leverage, along
[...] Read more.
We examine the key drivers behind management decisions on share repurchase from various theoretical perspectives, including the free cash flow theory and the signaling theory/hypothesis. Specifically, we investigate the relationship between share repurchase and three key drivers: surplus cash, undervaluation, and leverage, along with several control variables. Using a sample of UK-listed non-financial companies from 2012 to 2022, we apply logistic regression, standard OLS regression, and Tobit regression to identify the factors influencing share repurchase. Our findings reveal that firms repurchase shares to distribute cash to shareholders with surplus cash and Surplus investing cash flow. This study also finds that undervalued smaller firms with lower market-to-book ratios and lower leverage are more likely to repurchase shares. Our study highlights the key factors motivating companies’ share repurchases, such as undervaluation, surplus cash, and leverage, examined from various theoretical perspectives, including the free cash flow theory and signaling theory. Focusing on the UK context, as well as adding a new angle in regard to applying logistic regression, standard OLS regression, and Tobit regression in combination, this research contributes to the existing body of knowledge in corporate finance. The outcome of the study has plausible implications for financial managers and investors in selecting stocks. Its practical implications will help investors gain a better understanding of the factors and forces influencing share repurchase decisions.
Full article
Open AccessReview
Community-Based Disaster Insurance for Sustainable Economic Loss Risk Mitigation: A Systematic Literature Review
by
Titi Purwandari, Hilda Azkiyah Surya, Riaman, Yuyun Hidayat, Sukono and Moch Panji Agung Saputra
Risks 2024, 12(10), 158; https://doi.org/10.3390/risks12100158 - 7 Oct 2024
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This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel
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This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel perspective, highlighting their effectiveness in mitigating catastrophe risks. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was employed to systematically collect and analyze articles sourced from the Scopus, ScienceDirect, and Dimensions databases. The findings provide a comprehensive summary of the CBCI implementation, including various considerations such as risk-sharing mechanisms, premium determination, and policy frameworks. This research offers a fresh perspective on CBCI as a sustainable approach to catastrophe risk mitigation, contributing valuable insights to policymakers, practitioners, and researchers interested in community resilience and disaster risk management.
Full article
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Figure 1
<p>CBCI Model. (source: <a href="#B11-risks-12-00158" class="html-bibr">Bernhardt and Sykes</a> (<a href="#B11-risks-12-00158" class="html-bibr">2021</a>)).</p> Full article ">Figure 2
<p>PRISMA diagram of article data collection and selection process.</p> Full article ">Figure 3
<p>(<b>a</b>) Affiliated countries of the authors of the selected articles; (<b>b</b>) 10 countries with highest indices of exposure (source: World Map of Risk 2023 by Bündnis Entwicklung Hilft 2023, <a href="https://weltrisikobericht.de/en/" target="_blank">https://weltrisikobericht.de/en/</a> (accessed on 1 July 2024)).</p> Full article ">Figure 4
<p>Documents about CBCI per year.</p> Full article ">Figure 5
<p>(<b>a</b>) Network visualization of relationships between co-occurrence keywords; (<b>b</b>) overlay visualization of relationship between co-occurrence keywords; (<b>c</b>) network visualization of text data based on relationship between words in titles and abstracts; (<b>d</b>) overlay visualization of text data based on relationship between words in titles and abstracts.</p> Full article ">Figure 6
<p>Categories of CBCI article topics.</p> Full article ">Figure 7
<p>Methods to Determine Premium of CBCI.</p> Full article ">Figure 8
<p>Factors influencing the sustainability of CBCI.</p> Full article ">
<p>CBCI Model. (source: <a href="#B11-risks-12-00158" class="html-bibr">Bernhardt and Sykes</a> (<a href="#B11-risks-12-00158" class="html-bibr">2021</a>)).</p> Full article ">Figure 2
<p>PRISMA diagram of article data collection and selection process.</p> Full article ">Figure 3
<p>(<b>a</b>) Affiliated countries of the authors of the selected articles; (<b>b</b>) 10 countries with highest indices of exposure (source: World Map of Risk 2023 by Bündnis Entwicklung Hilft 2023, <a href="https://weltrisikobericht.de/en/" target="_blank">https://weltrisikobericht.de/en/</a> (accessed on 1 July 2024)).</p> Full article ">Figure 4
<p>Documents about CBCI per year.</p> Full article ">Figure 5
<p>(<b>a</b>) Network visualization of relationships between co-occurrence keywords; (<b>b</b>) overlay visualization of relationship between co-occurrence keywords; (<b>c</b>) network visualization of text data based on relationship between words in titles and abstracts; (<b>d</b>) overlay visualization of text data based on relationship between words in titles and abstracts.</p> Full article ">Figure 6
<p>Categories of CBCI article topics.</p> Full article ">Figure 7
<p>Methods to Determine Premium of CBCI.</p> Full article ">Figure 8
<p>Factors influencing the sustainability of CBCI.</p> Full article ">
Open AccessFeature PaperArticle
Advantages of Accounting for Stochasticity in the Premium Process
by
Yang Miao and Kristina P. Sendova
Risks 2024, 12(10), 157; https://doi.org/10.3390/risks12100157 - 3 Oct 2024
Abstract
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In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium
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In this paper, we study a risk model with stochastic premium income and its impact on solvency risk management. It is assumed that both the premium arrival process and the claim arrival process are modelled by homogeneous Poisson processes, and that the premium amounts are modelled by independent and identically distributed random variables. While this model has been studied in the existing literature and certain explicit results are known under more restrictive assumptions, these results are relatively difficult to apply in practice. In this paper, we investigate the factors that differentiate this model and the classical risk model. After reviewing various known results of this model, we derive a simulation approach for obtaining the probability of ultimate ruin based on importance sampling, which does not require specific distributions for the premium and the claim. We demonstrate this approach first with examples where the distribution of the sampling random variable can be identified. We then provide additional examples where we use the fast Fourier transform to obtain an approximation of the sampling random variable. The simulated results are compared with the known results for the probability of ruin. Using the simulation approach, we apply this model to a real-life auto-insurance data set. Differences with the classical model are then discussed. Finally, we comment on the suitability and impact of using this model in the context of solvency risk management.
Full article
Figure 1
Figure 1
<p>A sample trajectory of the surplus process.</p> Full article ">Figure 2
<p>(<b>Left</b>): an example of Equation (<a href="#FD16-risks-12-00157" class="html-disp-formula">16</a>); (<b>right</b>): a comparison of adjustment coefficients under the classical model and under the stochastic premium model.</p> Full article ">Figure 3
<p>Simulated probability of ruin vs. theoretical probability of ruin when both premiums and claims follow exponential distributions.</p> Full article ">Figure 4
<p>Simulated ruin probability vs. theoretical ruin probability when premiums are fixed amounts.</p> Full article ">Figure 5
<p>Fast Fourier transform with aliasing error.</p> Full article ">Figure 6
<p>Simulated ruin probability with gamma distributed claim sizes.</p> Full article ">Figure 7
<p>(<b>Left</b>): histogram of the observed premium amounts with fitted density function. (<b>Right</b>): empirical distribution of the claim sizes.</p> Full article ">Figure 8
<p>Daily claim rate per exposure.</p> Full article ">Figure 9
<p>Comparison of probability of ruin under the classical model and the stochastic premium model using the auto-insurance data set.</p> Full article ">Figure 10
<p>Total annual inflation-adjusted catastrophic losses and number of catastrophic events in Canada.</p> Full article ">
<p>A sample trajectory of the surplus process.</p> Full article ">Figure 2
<p>(<b>Left</b>): an example of Equation (<a href="#FD16-risks-12-00157" class="html-disp-formula">16</a>); (<b>right</b>): a comparison of adjustment coefficients under the classical model and under the stochastic premium model.</p> Full article ">Figure 3
<p>Simulated probability of ruin vs. theoretical probability of ruin when both premiums and claims follow exponential distributions.</p> Full article ">Figure 4
<p>Simulated ruin probability vs. theoretical ruin probability when premiums are fixed amounts.</p> Full article ">Figure 5
<p>Fast Fourier transform with aliasing error.</p> Full article ">Figure 6
<p>Simulated ruin probability with gamma distributed claim sizes.</p> Full article ">Figure 7
<p>(<b>Left</b>): histogram of the observed premium amounts with fitted density function. (<b>Right</b>): empirical distribution of the claim sizes.</p> Full article ">Figure 8
<p>Daily claim rate per exposure.</p> Full article ">Figure 9
<p>Comparison of probability of ruin under the classical model and the stochastic premium model using the auto-insurance data set.</p> Full article ">Figure 10
<p>Total annual inflation-adjusted catastrophic losses and number of catastrophic events in Canada.</p> Full article ">
Open AccessArticle
Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction
by
Johanna M. Orozco-Castañeda, Sebastián Alzate-Vargas and Danilo Bedoya-Valencia
Risks 2024, 12(10), 156; https://doi.org/10.3390/risks12100156 - 30 Sep 2024
Abstract
This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the
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This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.
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(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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<p>Typical ANFIS structure. Adapted from <a href="#B13-risks-12-00156" class="html-bibr">Jang</a> (<a href="#B13-risks-12-00156" class="html-bibr">1993</a>).</p> Full article ">Figure 2
<p>Architecture for an ANFIS with four rules.</p> Full article ">Figure 3
<p>BTC/USD price vs forecast from ARIMA model.</p> Full article ">Figure 4
<p>Scatterplot for squared returns.</p> Full article ">Figure 5
<p>Predictions in the testing sample with ANFIS and GARCH.</p> Full article ">Figure 6
<p>Predictions and confidence intervals in the testing sample with ANFIS.</p> Full article ">Figure 7
<p>Predictions and confidence intervals for the testing sample using GARCH(1,1).</p> Full article ">
<p>Typical ANFIS structure. Adapted from <a href="#B13-risks-12-00156" class="html-bibr">Jang</a> (<a href="#B13-risks-12-00156" class="html-bibr">1993</a>).</p> Full article ">Figure 2
<p>Architecture for an ANFIS with four rules.</p> Full article ">Figure 3
<p>BTC/USD price vs forecast from ARIMA model.</p> Full article ">Figure 4
<p>Scatterplot for squared returns.</p> Full article ">Figure 5
<p>Predictions in the testing sample with ANFIS and GARCH.</p> Full article ">Figure 6
<p>Predictions and confidence intervals in the testing sample with ANFIS.</p> Full article ">Figure 7
<p>Predictions and confidence intervals for the testing sample using GARCH(1,1).</p> Full article ">
Open AccessArticle
Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs
by
Leila Hamilton-Russell, Thomas Malan O’Callaghan, Dmitrii Savin and Erik Schlögl
Risks 2024, 12(10), 155; https://doi.org/10.3390/risks12100155 - 30 Sep 2024
Abstract
Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also
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Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.
Full article
(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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<p>Total cost for each index and all cases of <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. Here, the green line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, the orange line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and the red line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000. Each line corresponds to one independent run of the algorithm.</p> Full article ">Figure 2
<p>Changes in the composition of the tracking portfolio for the DAX index.</p> Full article ">Figure A1
<p>DAX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A2
<p>HSI Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A3
<p>JALSH Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A4
<p>SENSEX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A5
<p>SMI Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A6
<p>SPTSX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A7
<p>SPX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A8
<p>UKX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A9
<p>CAC Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A10
<p>IBEX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A11
<p>IBOV Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A12
<p>IPSA Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A13
<p>NKY Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A14
<p>SHSZ300 Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A15
<p>TWSE Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A16
<p>Cumulative transaction costs paid. Here, green is for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, orange is <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and red is <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000.</p> Full article ">Figure A17
<p>Changes in the composition of the tracking portfolio for the HSI index.</p> Full article ">Figure A18
<p>Changes in the composition of the tracking portfolio for the JALSH index.</p> Full article ">Figure A19
<p>Changes in the composition of the tracking portfolio for the SENSEX index.</p> Full article ">Figure A20
<p>Changes in the composition of the tracking portfolio for the SMI index.</p> Full article ">Figure A21
<p>Changes in the composition of the tracking portfolio for the SPTSX index.</p> Full article ">Figure A22
<p>Changes in the composition of the tracking portfolio for the SPX index.</p> Full article ">Figure A23
<p>Changes in the composition of the tracking portfolio for the UKX index.</p> Full article ">Figure A24
<p>CAC Mean Assets retention.</p> Full article ">Figure A25
<p>HSI Mean Assets retention.</p> Full article ">Figure A26
<p>JALSH Mean Assets retention.</p> Full article ">Figure A27
<p>SENSEX Mean Assets retention.</p> Full article ">Figure A28
<p>SMI Mean Assets retention.</p> Full article ">Figure A29
<p>SPTSX Mean Assets retention.</p> Full article ">Figure A30
<p>SPX Mean Assets retention.</p> Full article ">Figure A31
<p>UKX Mean Assets retention.</p> Full article ">Figure A32
<p>DAX Mean Assets retention.</p> Full article ">Figure A33
<p>IBEX Mean Assets retention.</p> Full article ">Figure A34
<p>IBOV Mean Assets retention.</p> Full article ">Figure A35
<p>IPSA Mean Assets retention.</p> Full article ">Figure A36
<p>NKY Mean Assets retention.</p> Full article ">Figure A37
<p>SHSZ300 Mean Assets retention.</p> Full article ">Figure A38
<p>TWSE Mean Assets retention.</p> Full article ">
<p>Total cost for each index and all cases of <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. Here, the green line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, the orange line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and the red line represents <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000. Each line corresponds to one independent run of the algorithm.</p> Full article ">Figure 2
<p>Changes in the composition of the tracking portfolio for the DAX index.</p> Full article ">Figure A1
<p>DAX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A2
<p>HSI Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A3
<p>JALSH Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A4
<p>SENSEX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A5
<p>SMI Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A6
<p>SPTSX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A7
<p>SPX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A8
<p>UKX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A9
<p>CAC Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A10
<p>IBEX Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A11
<p>IBOV Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A12
<p>IPSA Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A13
<p>NKY Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A14
<p>SHSZ300 Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A15
<p>TWSE Tracking portfolio return (blue) versus actual index returns (red).</p> Full article ">Figure A16
<p>Cumulative transaction costs paid. Here, green is for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, orange is <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math> and red is <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> </mrow> </semantics></math> 10,000.</p> Full article ">Figure A17
<p>Changes in the composition of the tracking portfolio for the HSI index.</p> Full article ">Figure A18
<p>Changes in the composition of the tracking portfolio for the JALSH index.</p> Full article ">Figure A19
<p>Changes in the composition of the tracking portfolio for the SENSEX index.</p> Full article ">Figure A20
<p>Changes in the composition of the tracking portfolio for the SMI index.</p> Full article ">Figure A21
<p>Changes in the composition of the tracking portfolio for the SPTSX index.</p> Full article ">Figure A22
<p>Changes in the composition of the tracking portfolio for the SPX index.</p> Full article ">Figure A23
<p>Changes in the composition of the tracking portfolio for the UKX index.</p> Full article ">Figure A24
<p>CAC Mean Assets retention.</p> Full article ">Figure A25
<p>HSI Mean Assets retention.</p> Full article ">Figure A26
<p>JALSH Mean Assets retention.</p> Full article ">Figure A27
<p>SENSEX Mean Assets retention.</p> Full article ">Figure A28
<p>SMI Mean Assets retention.</p> Full article ">Figure A29
<p>SPTSX Mean Assets retention.</p> Full article ">Figure A30
<p>SPX Mean Assets retention.</p> Full article ">Figure A31
<p>UKX Mean Assets retention.</p> Full article ">Figure A32
<p>DAX Mean Assets retention.</p> Full article ">Figure A33
<p>IBEX Mean Assets retention.</p> Full article ">Figure A34
<p>IBOV Mean Assets retention.</p> Full article ">Figure A35
<p>IPSA Mean Assets retention.</p> Full article ">Figure A36
<p>NKY Mean Assets retention.</p> Full article ">Figure A37
<p>SHSZ300 Mean Assets retention.</p> Full article ">Figure A38
<p>TWSE Mean Assets retention.</p> Full article ">
Open AccessArticle
Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory
by
Marc Cortés Rufé and Jordi Martí Pidelaserra
Risks 2024, 12(10), 154; https://doi.org/10.3390/risks12100154 - 29 Sep 2024
Abstract
In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial
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In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.
Full article
(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
Open AccessArticle
Transmuted Distortion Functions for Measuring Risks
by
Muna Alkasasbeh, Carl Lee and Felix Famoye
Risks 2024, 12(10), 153; https://doi.org/10.3390/risks12100153 - 26 Sep 2024
Abstract
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This paper introduces a new family of distortion functions for measuring risks, developed using transmutation techniques. We identify the parameter spaces where the proposed distortions exhibit concavity. Considering that the choice of distortion parameters can be influenced by political factors or users’ risk
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This paper introduces a new family of distortion functions for measuring risks, developed using transmutation techniques. We identify the parameter spaces where the proposed distortions exhibit concavity. Considering that the choice of distortion parameters can be influenced by political factors or users’ risk aversion levels, we generate plots of the distortion functions to examine how these parameters impact the tasks and users’ attitudes toward risk. The coherent properties of the resulting risk measures are explored, outlining the conditions under which the transmuted Kumaraswamy and transmuted truncated normal distortions ensure coherence. Numerical analyses demonstrate the effects of parameter variations on the derived risk measures, highlighting the effectiveness of the proposed distortion functions in accurately assessing risk.
Full article
Figure 1
Figure 1
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> from 1 to 7, where <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> are fixed at 0.5.</p> Full article ">Figure 2
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math>, with fixed <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> at 1.</p> Full article ">Figure 3
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, with fixed <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> at 1.</p> Full article ">Figure 4
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> is 0.5.</p> Full article ">Figure 5
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p> Full article ">Figure 6
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>μ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math> are fixed at 0.5.</p> Full article ">Figure 7
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>PDF of exponential, Lomax, and log-normal losses.</p> Full article ">
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> from 1 to 7, where <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> are fixed at 0.5.</p> Full article ">Figure 2
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math>, with fixed <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> at 1.</p> Full article ">Figure 3
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, with fixed <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> at 1.</p> Full article ">Figure 4
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>K</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>,</mo> <mi>λ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> is 0.5.</p> Full article ">Figure 5
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> <mo>.</mo> </mrow> </semantics></math></p> Full article ">Figure 6
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>μ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>σ</mi> </mrow> </semantics></math> are fixed at 0.5.</p> Full article ">Figure 7
<p>The distortion <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> <mo>−</mo> <mi>T</mi> <mi>N</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> <mo>;</mo> <mi>λ</mi> <mo>,</mo> <mo> </mo> <mi>μ</mi> <mo>,</mo> <mi>σ</mi> </mrow> </mfenced> </mrow> </semantics></math> for different values of <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>0.5</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>PDF of exponential, Lomax, and log-normal losses.</p> Full article ">
Open AccessArticle
A Contrast-Tree-Based Approach to Two-Population Models
by
Matteo Lizzi
Risks 2024, 12(10), 152; https://doi.org/10.3390/risks12100152 - 25 Sep 2024
Abstract
Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with
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Building small-population mortality tables has great practical importance in actuarial applications. In recent years, several works in the literature have explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations, and many models dealing with this problem usually use a two-population approach, modeling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small-population mortality tables, a general structure for a two-step two-population model is proposed, its main element of novelty residing in a machine-learning-based approach to mortality spread estimation. In order to obtain this, Contrast Trees and the related Estimation Contrast Boosting techniques have been applied. A quite general machine-learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from the Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model.
Full article
(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)
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<p>The ECB adaptation process for small-population mortality tables.</p> Full article ">Figure 2
<p>Standardized Death Rates (SDR) by age group. (<b>a</b>) ages 30–50; (<b>b</b>) ages 50–70; (<b>c</b>) ages 70–90; and (<b>d</b>) ages 30–90.</p> Full article ">Figure 3
<p>Relative Mortality Measure (RMM) relative to ages 30–90.</p> Full article ">Figure 4
<p>Lack-of-fit curves for Estimation Contrast Boosting calibration period assessment. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 5
<p>Lack-of-fit curves for Estimation Contrast Boosting predictor assessment, relative to the three different design matrices taken into consideration. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 6
<p>LOF curves comparing mortality scaling and ECB mortality boosting. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 7
<p>Regions uncovered by the Contrast Tree on the input space relative to Austrian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">Figure 8
<p>Regions uncovered by the Contrast Tree on the input space relative to Slovenian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">Figure 9
<p>Regions uncovered by the Contrast Tree on the input space relative to Lithuanian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">
<p>The ECB adaptation process for small-population mortality tables.</p> Full article ">Figure 2
<p>Standardized Death Rates (SDR) by age group. (<b>a</b>) ages 30–50; (<b>b</b>) ages 50–70; (<b>c</b>) ages 70–90; and (<b>d</b>) ages 30–90.</p> Full article ">Figure 3
<p>Relative Mortality Measure (RMM) relative to ages 30–90.</p> Full article ">Figure 4
<p>Lack-of-fit curves for Estimation Contrast Boosting calibration period assessment. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 5
<p>Lack-of-fit curves for Estimation Contrast Boosting predictor assessment, relative to the three different design matrices taken into consideration. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 6
<p>LOF curves comparing mortality scaling and ECB mortality boosting. Plots refer, respectively, to Austrian (<b>a</b>), Slovenian (<b>b</b>) and Lithuanian (<b>c</b>) book populations.</p> Full article ">Figure 7
<p>Regions uncovered by the Contrast Tree on the input space relative to Austrian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">Figure 8
<p>Regions uncovered by the Contrast Tree on the input space relative to Slovenian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">Figure 9
<p>Regions uncovered by the Contrast Tree on the input space relative to Lithuanian book population. (<b>a</b>) shows discrepancy for ECB mortality adaptation, while (<b>b</b>) shows discrepancy for mortality scaling.</p> Full article ">
Open AccessArticle
The Impact of Value-Added Intellectual Capital on Corporate Performance: Cross-Sector Evidence
by
Darya Dancaková and Jozef Glova
Risks 2024, 12(10), 151; https://doi.org/10.3390/risks12100151 - 25 Sep 2024
Abstract
This study explores the relationship between intellectual capital (IC) and the financial performance of 250 publicly traded companies in France, Germany, and Switzerland from 2009 to 2018, addressing the gaps in prior research regarding the differential impacts of IC components across countries and
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This study explores the relationship between intellectual capital (IC) and the financial performance of 250 publicly traded companies in France, Germany, and Switzerland from 2009 to 2018, addressing the gaps in prior research regarding the differential impacts of IC components across countries and industries in Western and Central Europe. Using the Value-Added Intellectual Coefficient (VAIC™) approach, this study evaluates human capital efficiency (HCE), structural capital efficiency (SCE), and capital employed efficiency (CEE). Panel regression analyses at the country and industry levels were conducted to assess their effects on financial metrics, such as return on equity (ROE), return on assets (ROA), and asset turnover ratio (ATO). The findings reveal a significant positive association between SCE, CEE, and firm performance, with CEE showing the most substantial effect, while HCE had a relatively weaker impact. Additionally, the study uncovers a trade-off between the accumulation of patents and trademarks and short-term financial performance, raising new considerations for intellectual property management. This research contributes to the literature by providing a nuanced understanding of how IC components influence financial outcomes across different contexts and offers practical insights for firms aiming to optimize structural capital and capital-employed strategies for improved financial performance while acknowledging the limitations regarding the sample of publicly traded firms.
Full article
(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
Open AccessArticle
Risk Management in Product Diversification: The Role of Managerial Overconfidence in Cost Stickiness—Evidence from Iran
by
Mona Parsaei, Davood Askarany, Mahtab Maleki and Ali Rahmani
Risks 2024, 12(10), 150; https://doi.org/10.3390/risks12100150 - 24 Sep 2024
Abstract
Purpose: This study investigates the relationship between product diversification strategy and cost stickiness, focusing on managerial overconfidence as a moderating factor. It aims to address a critical gap in the literature by providing empirical insights grounded in the Resource-Based View (RBV) theory, specifically
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Purpose: This study investigates the relationship between product diversification strategy and cost stickiness, focusing on managerial overconfidence as a moderating factor. It aims to address a critical gap in the literature by providing empirical insights grounded in the Resource-Based View (RBV) theory, specifically examining firms listed on the Tehran Stock Exchange. Methodology: Utilizing a sample of 149 companies from the Tehran Stock Exchange in Iran spanning from 2015 to 2021, this study tests two hypotheses: (1) a positive relationship between product diversification and cost stickiness and (2) the amplification of this relationship by managerial overconfidence. Product diversification is quantified using the Herfindahl Index, while managerial overconfidence is measured through an investment-based index derived from capital expenditures. Cost stickiness is assessed by analysing the asymmetric behaviour of costs in response to changes in sales, focusing on how costs tend to remain high even when sales decrease. Findings: The empirical results substantiate both hypotheses, demonstrating a significant positive relationship between product diversification strategy and cost stickiness. Furthermore, managerial overconfidence amplifies this relationship, highlighting the role of internal resources and managerial perceptions in shaping cost behaviour. Originality: This study contributes substantially to the literature by being among the first to empirically examine the interplay between product diversification strategy, cost stickiness, and managerial overconfidence. Extending the RBV theory to cost behaviour and strategic management provides novel insights for scholars and practitioners in entrepreneurship, corporate strategy, and organizational behaviour. The findings underscore the importance of strategic choices and managerial traits in determining cost stickiness, offering valuable implications for financial analysts, auditors, and stakeholders.
Full article
(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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Open AccessArticle
Funding Illiquidity Implied by S&P 500 Derivatives
by
Benjamin Golez, Jens Jackwerth and Anna Slavutskaya
Risks 2024, 12(9), 149; https://doi.org/10.3390/risks12090149 - 18 Sep 2024
Abstract
Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding
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Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding illiquidity earn high returns in normal times and low returns in crisis periods when funding liquidity deteriorates. The results are not driven by existing measures of funding illiquidity, market illiquidity, and proxies for tail risk. Our funding illiquidity measure also affects leveraged closed-end mutual funds and, to an extent, asset classes where leveraged investors are marginal investors.
Full article
(This article belongs to the Special Issue Financial Derivatives and Their Applications)
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<p>Funding illiquidity measure. (We plot the time-series of the investor’s implied borrowing rate (<b>A</b>), the investor’s mid-point rate along with the LIBOR (<b>B</b>), and the difference between the implied borrowing rate and the mid-point rate—our funding illiquidity measure (<b>C</b>). All rates have a constant three-month maturity. The period is from January 1994 to December 2012.)</p> Full article ">Figure 1 Cont.
<p>Funding illiquidity measure. (We plot the time-series of the investor’s implied borrowing rate (<b>A</b>), the investor’s mid-point rate along with the LIBOR (<b>B</b>), and the difference between the implied borrowing rate and the mid-point rate—our funding illiquidity measure (<b>C</b>). All rates have a constant three-month maturity. The period is from January 1994 to December 2012.)</p> Full article ">Figure 2
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 3
<p>Hedge fund return spread. (We plot the time-series of the spread return between the hedge funds portfolio 1 and portfolio 10 along with the NBER recession periods (in gray). The figure is based on the twelve-month moving average return of the hedge fund return spread. The return spread is expressed in percentages.)</p> Full article ">
<p>Funding illiquidity measure. (We plot the time-series of the investor’s implied borrowing rate (<b>A</b>), the investor’s mid-point rate along with the LIBOR (<b>B</b>), and the difference between the implied borrowing rate and the mid-point rate—our funding illiquidity measure (<b>C</b>). All rates have a constant three-month maturity. The period is from January 1994 to December 2012.)</p> Full article ">Figure 1 Cont.
<p>Funding illiquidity measure. (We plot the time-series of the investor’s implied borrowing rate (<b>A</b>), the investor’s mid-point rate along with the LIBOR (<b>B</b>), and the difference between the implied borrowing rate and the mid-point rate—our funding illiquidity measure (<b>C</b>). All rates have a constant three-month maturity. The period is from January 1994 to December 2012.)</p> Full article ">Figure 2
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 2 Cont.
<p>Funding illiquidity versus alternative measures. (We plot the time-series plots of funding illiquidity along with the absolute or relative SP futures margin (<b>A</b>), relative or absolute bid–ask spread (<b>B</b>), funding illiquidity along with the net demand or VIX (<b>C</b>), funding illiquidity along with the term spread or default spread (<b>D</b>), and funding illiquidity along with the TED spread or LIBOR–repo spread (<b>E</b>). The period is from January 1994 to December 2012.)</p> Full article ">Figure 3
<p>Hedge fund return spread. (We plot the time-series of the spread return between the hedge funds portfolio 1 and portfolio 10 along with the NBER recession periods (in gray). The figure is based on the twelve-month moving average return of the hedge fund return spread. The return spread is expressed in percentages.)</p> Full article ">
Open AccessArticle
Automated Machine Learning and Asset Pricing
by
Jerome V. Healy, Andros Gregoriou and Robert Hudson
Risks 2024, 12(9), 148; https://doi.org/10.3390/risks12090148 - 14 Sep 2024
Abstract
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural
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We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets.
Full article
(This article belongs to the Special Issue Portfolio Selection and Asset Pricing)
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<p>Neural network architectures. Source: <a href="#B7-risks-12-00148" class="html-bibr">Dixon and Halperin</a> (<a href="#B7-risks-12-00148" class="html-bibr">2019</a>).</p> Full article ">Figure 2
<p>Deep learning applied to S&P500 index forecasting. Source: <a href="#B17-risks-12-00148" class="html-bibr">Heaton et al.</a> (<a href="#B17-risks-12-00148" class="html-bibr">2017</a>).</p> Full article ">
<p>Neural network architectures. Source: <a href="#B7-risks-12-00148" class="html-bibr">Dixon and Halperin</a> (<a href="#B7-risks-12-00148" class="html-bibr">2019</a>).</p> Full article ">Figure 2
<p>Deep learning applied to S&P500 index forecasting. Source: <a href="#B17-risks-12-00148" class="html-bibr">Heaton et al.</a> (<a href="#B17-risks-12-00148" class="html-bibr">2017</a>).</p> Full article ">
Open AccessArticle
Dynamics of Foreign Exchange Futures Trading Volumes in Thailand
by
Woradee Jongadsayakul
Risks 2024, 12(9), 147; https://doi.org/10.3390/risks12090147 - 14 Sep 2024
Abstract
Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading
[...] Read more.
Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability.
Full article
(This article belongs to the Special Issue Financial Derivatives: Market Risk, Pricing, and Hedging)
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<p>Daily trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures from 31 October 2022 to 12 January 2024.</p> Full article ">Figure 2
<p>Inverse roots of AR characteristic polynomial. Notes: <a href="#risks-12-00147-f002" class="html-fig">Figure 2</a> shows the inverse roots of the VAR(2) model. All roots have a modulus less than one and lie inside the unit circle, meaning that the VAR(2) model is variance and covariance stationary.</p> Full article ">Figure 3
<p>Impulse response function results. Notes: <a href="#risks-12-00147-f003" class="html-fig">Figure 3</a> shows the results of the response of trading volume of each FX futures contract, EUR/USD futures in panel (<b>a</b>), USD/JPY futures in panel (<b>b</b>), and USD/THB futures in panel (<b>c</b>) to a one-standard-deviation shock in its own innovation and to changes in trading volumes of other FX futures. The response of FX futures trading volume to its own shock is positive and high. The USD/JPY futures trading volume has a negative impact on USD/THB futures trading volume, and vice versa.</p> Full article ">
<p>Daily trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures from 31 October 2022 to 12 January 2024.</p> Full article ">Figure 2
<p>Inverse roots of AR characteristic polynomial. Notes: <a href="#risks-12-00147-f002" class="html-fig">Figure 2</a> shows the inverse roots of the VAR(2) model. All roots have a modulus less than one and lie inside the unit circle, meaning that the VAR(2) model is variance and covariance stationary.</p> Full article ">Figure 3
<p>Impulse response function results. Notes: <a href="#risks-12-00147-f003" class="html-fig">Figure 3</a> shows the results of the response of trading volume of each FX futures contract, EUR/USD futures in panel (<b>a</b>), USD/JPY futures in panel (<b>b</b>), and USD/THB futures in panel (<b>c</b>) to a one-standard-deviation shock in its own innovation and to changes in trading volumes of other FX futures. The response of FX futures trading volume to its own shock is positive and high. The USD/JPY futures trading volume has a negative impact on USD/THB futures trading volume, and vice versa.</p> Full article ">
Open AccessArticle
What Drives Banks to Provide Green Loans? Corporate Governance and Ownership Structure Perspectives of Vietnamese Listed Banks
by
Ariful Hoque, Duong Thuy Le and Thi Le
Risks 2024, 12(9), 146; https://doi.org/10.3390/risks12090146 - 13 Sep 2024
Abstract
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This study delves into the influence of banks’ governance and ownership structures on green lending. To examine this, we utilized the two-step system GMM and PCSE methods on the panel data of Vietnamese commercial banks spanning from 2010 to 2023. The findings suggest
[...] Read more.
This study delves into the influence of banks’ governance and ownership structures on green lending. To examine this, we utilized the two-step system GMM and PCSE methods on the panel data of Vietnamese commercial banks spanning from 2010 to 2023. The findings suggest that board characteristics, precisely board size, board independence, and gender diversity, play a significant role in encouraging banks to provide green credit. The study highlights the importance of ownership structure in green lending. Banks with a high percentage of government ownership tend to fund more green projects, while foreign counterparts are reluctant to fund green finance. A mechanism test is also conducted to point out that banks’ disclosure of their green loan commitments is an influential channel whereby corporate governance and ownership structure impact green loans. Additionally, this research finds that the issuance of the Green Loan Principles in 2018 can facilitate banks’ governance of sustainable lending.
Full article
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