Risks

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 2631

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 - 06 Jun 2023

Cited by 5 | Viewed by 12182

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 - 01 Jun 2023

Cited by 6 | Viewed by 7913

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 5 | Viewed by 4834

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 5 | Viewed by 1615

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 3106

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 - 09 Mar 2023

Cited by 2 | Viewed by 1778

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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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

Viewed by 1209

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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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 6 | Viewed by 9030

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)

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 3 | Viewed by 3217

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 - 09 Feb 2023

Cited by 1 | Viewed by 1367

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

Viewed by 1915

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 - 09 Jan 2023

Cited by 13 | Viewed by 3331

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 - 06 Jan 2023

Cited by 1 | Viewed by 1687

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 - 04 Jan 2023

Cited by 4 | Viewed by 2397

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 - 09 Nov 2022

Cited by 11 | Viewed by 2489

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 24 | Viewed by 7196

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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20 pages, 1041 KiB

Open AccessEditor’s ChoiceArticle

The Impact of Corporate Social Responsibility and Innovative Strategies on Financial Performance

by Joana Costa and José Pedro Fonseca

Risks 2022, 10(5), 103; https://doi.org/10.3390/risks10050103 - 12 May 2022

Cited by 13 | Viewed by 6006

Abstract

The article aims to appraise the role of Corporate Social Responsibility (CSR) and innovation strategies as leverages of a company’s financial performance. The theoretical and empirical statement of this link aims to reinforce the importance of these strategical options in both the managerial [...] Read more.

The article aims to appraise the role of Corporate Social Responsibility (CSR) and innovation strategies as leverages of a company’s financial performance. The theoretical and empirical statement of this link aims to reinforce the importance of these strategical options in both the managerial and the public policy domain. Shedding light on the economic return of these practices will help managers make better strategic decisions. Policy makers will also grasp the required evidence to encompass CSR in policy packages. To address the research question, data were collected from the Thomson Reuters Eikon Datastream covering the 1000 largest companies listed on the stock exchange worldwide. Thereafter, hierarchical linear regressions were performed to produce the econometric results. Two time frames (2015–2019) were compared to address time–space trends. Enrolling in CSR activities entails additional costs which can undermine the company’s financial performance if not properly supported by public policies. Combining CSR and innovation appears to be the best strategy for companies seeking improvements in their financial performance while being socially responsible. The contribution of this study is threefold: first, the analysis covers the largest thousand firms in operation worldwide; secondly, the econometric results demonstrate that combining CSR with innovation positively impacts financial performance; and lastly, the time comparison evidences a positive but slow evolution in CSR adoption. The article provides an applied perspective, of use both for managers and policy makers, as to how they should approach and disseminate involvement in these types of activities. Full article

(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development)

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13 pages, 506 KiB

Open AccessEditor’s ChoiceArticle

Non-Performing Loans and Macroeconomics Factors: The Italian Case

by Matteo Foglia

Risks 2022, 10(1), 21; https://doi.org/10.3390/risks10010021 - 12 Jan 2022

Cited by 25 | Viewed by 8531

Abstract

The purpose of this work is to investigate the influence of macroeconomics determinants on non-performing loans (NPLs) in the Italian banking system over the period 2008Q3–2020Q4. We mainly contribute to the literature by being the first empirical article to study this relationship in [...] Read more.

The purpose of this work is to investigate the influence of macroeconomics determinants on non-performing loans (NPLs) in the Italian banking system over the period 2008Q3–2020Q4. We mainly contribute to the literature by being the first empirical article to study this relationship in the Italian context in the recent period, thus providing fresh evidence on the macroeconomic impact on NPLs, i.e., on the credit risk of Italian banks. By employing the Autoregressive Distributed Lag (ARDL) cointegration model, we are able to investigate the short and long-run effects of macroeconomic factors on NPLs. The empirical findings show that gross domestic product and public debt have a negative impact on NPLs. On the other hand, we find that the unemployment rate and domestic credit positively influence impaired loans. Finally, we find evidence of the “gamble for resurrection” approach, i.e., Italian banks tend to support “zombie firms”. Full article

(This article belongs to the Special Issue Credit Risk Management)

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15 pages, 1311 KiB

Open AccessEditor’s ChoiceArticle

A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets

by Yianni Doumenis, Javad Izadi, Pradeep Dhamdhere, Epameinondas Katsikas and Dimitrios Koufopoulos

Risks 2021, 9(11), 207; https://doi.org/10.3390/risks9110207 - 12 Nov 2021

Cited by 11 | Viewed by 7053

Abstract

The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially [...] Read more.

The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability. Full article

(This article belongs to the Special Issue Cryptocurrencies and Risk Management)

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23 pages, 692 KiB

Open AccessEditor’s ChoiceArticle

ESG-Washing in the Mutual Funds Industry? From Information Asymmetry to Regulation

by Bertrand Candelon, Jean-Baptiste Hasse and Quentin Lajaunie

Risks 2021, 9(11), 199; https://doi.org/10.3390/risks9110199 - 05 Nov 2021

Cited by 10 | Viewed by 7326

Abstract

In this paper, we study the asymmetric information between asset managers and investors in the socially responsible investment (SRI) market. Specifically, we investigate the lack of transparency of the extra-financial information communicated by asset managers. Using a unique international panel dataset of approximately [...] Read more.

In this paper, we study the asymmetric information between asset managers and investors in the socially responsible investment (SRI) market. Specifically, we investigate the lack of transparency of the extra-financial information communicated by asset managers. Using a unique international panel dataset of approximately 1500 equity mutual funds, we provide empirical evidence that some asset managers portray themselves as socially responsible yet do not make tangible investment decisions. Furthermore, our results indicate that the financial performance of mutual funds is not related to asset managers’ signals but should be evaluated relatively using extra-financial ratings. In summary, our findings advocate for a unified regulation framework that constrains asset managers’ communication. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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14 pages, 396 KiB

Open AccessEditor’s ChoiceArticle

ESG Disclosure and Portfolio Performance

by Ramón Bermejo Climent, Isabel Figuerola-Ferretti Garrigues, Ioannis Paraskevopoulos and Alvaro Santos

Risks 2021, 9(10), 172; https://doi.org/10.3390/risks9100172 - 24 Sep 2021

Cited by 21 | Viewed by 8257

Abstract

This paper illustrates the impact of Environmental Social and Governance (ESG) disclosure on European corporate equity performance. In this study, we use an extensive data set of European ESG ratings provided by Bloomberg to demonstrate that ESG disclosure is associated with improved return [...] Read more.

This paper illustrates the impact of Environmental Social and Governance (ESG) disclosure on European corporate equity performance. In this study, we use an extensive data set of European ESG ratings provided by Bloomberg to demonstrate that ESG disclosure is associated with improved return growth, with the Governance pillar exhibiting the strongest effect on corporate performance. The impact of ESG disclosure on volatility is changing over time, suggesting that the existence of opaque ratings limits the transmission of information disclosure into corporate performance. Full article

20 pages, 3016 KiB

Open AccessEditor’s ChoiceArticle

Sustainable Risk Management in IT Enterprises

by Mateusz Trzeciak

Risks 2021, 9(7), 135; https://doi.org/10.3390/risks9070135 - 15 Jul 2021

Cited by 9 | Viewed by 4396

Abstract

A synthesis of literature studies covering the determinants of agile project management methods, risk management processes as well as factors influencing the shaping of project success and failure clearly indicates that in most publications on risk in agile managed projects, the human factor [...] Read more.

A synthesis of literature studies covering the determinants of agile project management methods, risk management processes as well as factors influencing the shaping of project success and failure clearly indicates that in most publications on risk in agile managed projects, the human factor is heavily underestimated at the expense of often excessive favoring of procedures. Meanwhile, after analyzing the risk factors that arise in agile-managed IT projects, it became apparent that in addition to aspects such as technology, hardware, system, or even project schedule and cost, the project team is highlighted, which is also the second concept with the GPM P5 Standard for Sustainability in Project Management. Thus, the purpose of this article is to develop a model for risk management in IT projects. As a result of the empirical research carried out by means of an expert interview (108 experts) and a questionnaire survey (123 respondents), a risk management model was developed and six original risk management areas were identified, describing 73.92% of all risk factors that may occur during the implementation of an IT project. Furthermore, empirical studies confirm that basic processes such as risk factor identification, impact assessment, and key risk factor management are used by managers and/or team leaders during the implementation of IT projects. Full article

(This article belongs to the Special Issue Advances in Sustainable Risk Management)

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15 pages, 691 KiB

Open AccessEditor’s ChoiceArticle

A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies

by Andrada-Ioana Sabău (Popa), Codruța Mare and Ioana Lavinia Safta

Risks 2021, 9(6), 116; https://doi.org/10.3390/risks9060116 - 10 Jun 2021

Cited by 11 | Viewed by 5118

Abstract

Tax avoidance is one of the most frequent reasons for which companies tend to resort to creative accounting techniques. The purpose of the study is to identify which of the eight-variables from the Beneish influences the most or least the outcome of the [...] Read more.

Tax avoidance is one of the most frequent reasons for which companies tend to resort to creative accounting techniques. The purpose of the study is to identify which of the eight-variables from the Beneish influences the most or least the outcome of the final score, as a percent, by developing a statistical model. The sample was selected from the Bucharest Stock Exchange and consists of 66 companies traded on the main market, for the years 2015–2019. The results show that from the total of the eight variables, GMI (Gross Margin Index), AQI (Asset Quality Index), DEPI (Depreciation Index) and TATA (Total Accruals to Total Assets) are significantly influencing the probability to commit fraud. The developed model is validated with only 10% of the non-fraud companies being mistakenly considered as fraud based on our model and vice versa. Full article

(This article belongs to the Special Issue Economic and Financial Crimes)

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23 pages, 3258 KiB

Open AccessEditor’s ChoiceArticle

Credit Risk Management of Property Investments through Multi-Criteria Indicators

by Marco Locurcio, Francesco Tajani, Pierluigi Morano, Debora Anelli and Benedetto Manganelli

Risks 2021, 9(6), 106; https://doi.org/10.3390/risks9060106 - 02 Jun 2021

Cited by 17 | Viewed by 3830

Abstract

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital [...] Read more.

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur. Full article

(This article belongs to the Special Issue Credit Risk Management)

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17 pages, 406 KiB

Open AccessEditor’s ChoiceArticle

Risk Approach—Risk Hierarchy or Construction Investment Risks in the Light of Interim Empiric Primary Research Conclusions

by Tibor Pál Szemere, Mónika Garai-Fodor and Ágnes Csiszárik-Kocsir

Risks 2021, 9(5), 84; https://doi.org/10.3390/risks9050084 - 01 May 2021

Cited by 10 | Viewed by 2371

Abstract

The focus of this study is to examine the investment project process. Since investment can also be considered as economic interactions, certain risks are associated with their implementation. Risk factors were given a particular priority during the secondary and primary research, while determining [...] Read more.

The focus of this study is to examine the investment project process. Since investment can also be considered as economic interactions, certain risks are associated with their implementation. Risk factors were given a particular priority during the secondary and primary research, while determining the most relevant risk factors of investment project processes in relation to the B2B market. The risk map for investment project processes was created in line with the relevant secondary sources, qualitative and quantitative primary results. This is topical because the importance of investments is unquestionable in a market economy. Therefore, a comprehensive risk assessment might provide results that are useful for both supply and demand side actors in B2B market relations. Based on the results of the primary study, the perceived risks of the project process were defined, and they were structured into a risk hierarchy system. Based on the qualitative results, we performed a quantitative study. Based on the responses of the sample subjects, we determined the perceived risk factors, and on the basis of them, we segmented the service provider (contractor) market. The main socio-demographic characteristics of each segment were also explored in the framework of the research. Full article

11 pages, 709 KiB

Open AccessEditor’s ChoiceArticle

The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland

by Joanna Wieprow and Agnieszka Gawlik

Risks 2021, 9(4), 78; https://doi.org/10.3390/risks9040078 - 16 Apr 2021

Cited by 18 | Viewed by 2851

Abstract

The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is [...] Read more.

The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is used to select models appropriate to analyze the risk of bankruptcy of tourism enterprises listed on the Warsaw Stock Exchange (WSE). The data are from half-year financial statements (the first half of 2019 and 2020, respectively). The obtained results are compared with the current values of the Altman EM-score model and selected financial ratios. An analysis allowed the estimation of the risk of bankruptcy of enterprises from the tourism sector in Poland as well as the assessment of the prognostic value of these models in the tourism sector and the risk of a collapse of this market in Poland. The article fills the research gap created by the negligible use of solvency analysis of the tourism sector and constitutes the basis for estimating the risk of collapse of the tourism sector in a crisis situation. Full article

(This article belongs to the Special Issue Risk in Contemporary Management)

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20 pages, 642 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning Approach for Micro-Credit Scoring

by Apostolos Ampountolas, Titus Nyarko Nde, Paresh Date and Corina Constantinescu

Risks 2021, 9(3), 50; https://doi.org/10.3390/risks9030050 - 09 Mar 2021

Cited by 23 | Viewed by 14419

Abstract

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various [...] Read more.

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases. Full article

(This article belongs to the Special Issue Interplay between Financial and Actuarial Mathematics)

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19 pages, 530 KiB

Open AccessEditor’s ChoiceArticle

Mortality Forecasting with an Age-Coherent Sparse VAR Model

by Hong Li and Yanlin Shi

Risks 2021, 9(2), 35; https://doi.org/10.3390/risks9020035 - 05 Feb 2021

Cited by 11 | Viewed by 2503

Abstract

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs [...] Read more.

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs a rotation algorithm in the projection phase to generate age-coherent mortality forecasts. In the estimation phase, the age-specific mortality improvement rates are fitted to a VAR model with dimension reduction algorithms such as the elastic net. In the projection phase, the projected mortality improvement rates are assumed to follow a short-term fluctuation component and a long-term force of decay, and will eventually converge to an age-invariant mean in expectation. The age-invariance of the long-term mean guarantees age-coherent mortality projections. The proposed model is generalized to multi-population context in a computationally efficient manner. Using single-age, uni-sex mortality data of the UK and France, we show that the proposed model is able to generate more reasonable long-term projections, as well as more accurate short-term out-of-sample forecasts than popular existing mortality models under various settings. Therefore, the proposed model is expected to be an appealing alternative to existing mortality models in insurance and demographic analyses. Full article

(This article belongs to the Special Issue Mortality Forecasting and Applications)

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17 pages, 3439 KiB

Open AccessEditor’s ChoiceArticle

An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount

by George Tzougas and Himchan Jeong

Risks 2021, 9(1), 19; https://doi.org/10.3390/risks9010019 - 08 Jan 2021

Cited by 8 | Viewed by 2027

Abstract

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed [...] Read more.

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed claim sizes, as they both represent significant proportions of the total loss in non-life insurance. The model’s implementation is illustrated by a real data application which involves fitting claim size data from a European motor insurer. The maximum likelihood estimation of the model parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is demonstrated to perform satisfactorily. Full article

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16 pages, 537 KiB

Open AccessEditor’s ChoiceReview

Supply Chain Risk Management: Literature Review

by Amulya Gurtu and Jestin Johny

Risks 2021, 9(1), 16; https://doi.org/10.3390/risks9010016 - 06 Jan 2021

Cited by 75 | Viewed by 50981

Abstract

The risks associated with global supply chain management has created a discourse among practitioners and academics. This is evident by the business uncertainties growing in supply chain management, which pose threats to the entire network flow and economy. This paper aims to review [...] Read more.

The risks associated with global supply chain management has created a discourse among practitioners and academics. This is evident by the business uncertainties growing in supply chain management, which pose threats to the entire network flow and economy. This paper aims to review the existing literature on risk factors in supply chain management in an uncertain and competitive business environment. Papers that contained the word “risk” in their titles, keywords, or abstracts were selected for conducting the theoretical analyses. Supply chain risk management is an integral function of the supply network. It faces unpredictable challenges due to nations’ economic policies and globalization, which have raised uncertainty and challenges for supply chain organizations. These significantly affect the financial performance of the organizations and the economy of a nation. Debate on supply chain risk management may promote competitiveness in business. Risk mitigation strategies will reduce the impact caused due to natural and human-made disasters. Full article

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26 pages, 657 KiB

Open AccessEditor’s ChoiceReview

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

by Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne and Etienne Marceau

Risks 2021, 9(1), 4; https://doi.org/10.3390/risks9010004 - 23 Dec 2020

Cited by 28 | Viewed by 12267

Abstract

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer [...] Read more.

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry. Full article

(This article belongs to the Special Issue Data Mining in Actuarial Science: Theory and Applications)

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9 pages, 479 KiB

Open AccessEditor’s ChoiceArticle

Why to Buy Insurance? An Explainable Artificial Intelligence Approach

by Alex Gramegna and Paolo Giudici

Risks 2020, 8(4), 137; https://doi.org/10.3390/risks8040137 - 14 Dec 2020

Cited by 21 | Viewed by 4894

Abstract

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost [...] Read more.

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well. Full article

(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)

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18 pages, 585 KiB

Open AccessEditor’s ChoiceArticle

A Deep Neural Network Algorithm for Semilinear Elliptic PDEs with Applications in Insurance Mathematics

by Stefan Kremsner, Alexander Steinicke and Michaela Szölgyenyi

Risks 2020, 8(4), 136; https://doi.org/10.3390/risks8040136 - 09 Dec 2020

Cited by 14 | Viewed by 3211

Abstract

In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. [...] Read more.

In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. The solutions to such control problems correspond to solutions of deterministic semilinear (degenerate) elliptic partial differential equations. In the present paper we propose a novel deep neural network algorithm for solving such partial differential equations in high dimensions in order to be able to compute the proposed risk measure in a complex high-dimensional economic environment. The method is based on the correspondence of elliptic partial differential equations to backward stochastic differential equations with unbounded random terminal time. In particular, backward stochastic differential equations—which can be identified with solutions of elliptic partial differential equations—are approximated by means of deep neural networks. Full article

(This article belongs to the Special Issue Computational Finance and Risk Analysis in Insurance)

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21 pages, 552 KiB

Open AccessEditor’s ChoiceArticle

Price Formation and Optimal Trading in Intraday Electricity Markets with a Major Player

by Olivier Féron, Peter Tankov and Laura Tinsi

Risks 2020, 8(4), 133; https://doi.org/10.3390/risks8040133 - 07 Dec 2020

Cited by 13 | Viewed by 2831

Abstract

We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory, we identify the optimal strategies of [...] Read more.

We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory, we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in a closed form in the asymptotic framework of mean field games with a major player. Full article

(This article belongs to the Special Issue Stochastic Modeling and Pricing in Energy Markets)

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31 pages, 2911 KiB

Open AccessEditor’s ChoiceArticle

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

by Christa Cuchiero, Wahid Khosrawi and Josef Teichmann

Risks 2020, 8(4), 101; https://doi.org/10.3390/risks8040101 - 27 Sep 2020

Cited by 28 | Viewed by 4901

Abstract

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters [...] Read more.

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method. Full article

(This article belongs to the Special Issue Machine Learning in Finance, Insurance and Risk Management)

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23 pages, 673 KiB

Open AccessEditor’s ChoiceArticle

EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking

by George Tzougas

Risks 2020, 8(3), 97; https://doi.org/10.3390/risks8030097 - 11 Sep 2020

Cited by 15 | Viewed by 3266

Abstract

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying [...] Read more.

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters. Full article

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26 pages, 1640 KiB

Open AccessEditor’s ChoiceArticle

Nagging Predictors

by Ronald Richman and Mario V. Wüthrich

Risks 2020, 8(3), 83; https://doi.org/10.3390/risks8030083 - 04 Aug 2020

Cited by 33 | Viewed by 4572

Abstract

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results [...] Read more.

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the family of Tweedie’s compound Poisson models, which are usually used for general insurance pricing, are provided. In the context of a French motor third-party liability insurance example, the nagging predictor achieves stability at portfolio level after about 20 runs. At an insurance policy level, we show that for some policies up to 400 neural network runs are required to achieve stability. Since working with 400 neural networks is impractical, we calibrate two meta models to the nagging predictor, one unweighted, and one using the coefficient of variation of the nagging predictor as a weight, finding that these latter meta networks can approximate the nagging predictor well, only with a small loss of accuracy. Full article

(This article belongs to the Special Issue Computational Finance and Risk Analysis in Insurance)

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19 pages, 1053 KiB

Open AccessEditor’s ChoiceArticle

Neural Networks and Betting Strategies for Tennis

by Vincenzo Candila and Lucio Palazzo

Risks 2020, 8(3), 68; https://doi.org/10.3390/risks8030068 - 29 Jun 2020

Cited by 13 | Viewed by 5518

Abstract

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than [...] Read more.

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted. Full article

(This article belongs to the Special Issue Risks in Gambling)

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20 pages, 2027 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Heads and Tails of Earnings Management: Quantitative Analysis in Emerging Countries

by Pavol Durana, Katarina Valaskova, Darina Chlebikova, Vladislav Krastev and Irina Atanasova

Risks 2020, 8(2), 57; https://doi.org/10.3390/risks8020057 - 01 Jun 2020

Cited by 14 | Viewed by 4024

Abstract

Earnings management is a globally used tool for long-term profitable enterprises and for the apparatus of reduction of bankruptcy risk in developed countries. This phenomenon belongs to the integral and fundamental part of their business finance. However, this has still been lax in [...] Read more.

Earnings management is a globally used tool for long-term profitable enterprises and for the apparatus of reduction of bankruptcy risk in developed countries. This phenomenon belongs to the integral and fundamental part of their business finance. However, this has still been lax in emerging countries. The models of detections of the existence of earnings management are based on discretionary accrual. The goal of this article is to detect the existence of earnings management in emerging countries by times series analysis. This econometric investigation uses the observations of earnings before interest and taxes of 1089 Slovak enterprises and 1421 Bulgarian enterprises in financial modelling. Our findings confirm the significant existence of earnings management in both analyzed countries, based on a quantitative analysis of unit root and stationarity. The managerial activities are purposeful, which is proven by the existence of no stationarity in the time series and a clear occurrence of the unit root. In addition, the results highlight the year 2014 as a significant milestone of change in the development of earnings management in both countries, based on homogeneity analyses. These facts identify significant parallels between Slovak and Bulgarian economics and business finance. Full article

(This article belongs to the Special Issue Quantitative Methods in Economics and Finance)

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16 pages, 499 KiB

Open AccessEditor’s ChoiceArticle

Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

by Andrea Rigamonti

Risks 2020, 8(1), 29; https://doi.org/10.3390/risks8010029 - 14 Mar 2020

Cited by 9 | Viewed by 7022

Abstract

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize [...] Read more.

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified. Full article

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18 pages, 452 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Rational Savings Account Models for Backward-Looking Interest Rate Benchmarks

by Andrea Macrina and David Skovmand

Risks 2020, 8(1), 23; https://doi.org/10.3390/risks8010023 - 03 Mar 2020

Cited by 9 | Viewed by 3371

Abstract

Interest rate benchmarks are currently undergoing a major transition. The LIBOR benchmark is planned to be discontinued by the end of 2021 and superseded by what ISDA calls an adjusted risk-free rate (RFR). ISDA has recently announced that the LIBOR replacement will most [...] Read more.

Interest rate benchmarks are currently undergoing a major transition. The LIBOR benchmark is planned to be discontinued by the end of 2021 and superseded by what ISDA calls an adjusted risk-free rate (RFR). ISDA has recently announced that the LIBOR replacement will most likely be constructed from a compounded running average of RFR overnight rates over a period matching the LIBOR tenor. This new backward-looking benchmark is markedly different when compared with LIBOR. It is measurable only at the end of the term in contrast to the forward-looking LIBOR, which is measurable at the start of the term. The RFR provides a simplification because the cash flows and the discount factors may be derived from the same discounting curve, thus avoiding—on a superficial level—any multi-curve complications. We develop a new class of savings account models and derive a novel interest rate system specifically designed to facilitate a high degree of tractability for the pricing of RFR-based fixed-income instruments. The rational form of the savings account models under the risk-neutral measure enables the pricing in closed form of caplets, swaptions and futures written on the backward-looking interest rate benchmark. Full article

(This article belongs to the Special Issue Interest Rate Risk Modelling in Transformation)

27 pages, 2013 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

by Mathias Bärtl and Simone Krummaker

Risks 2020, 8(1), 22; https://doi.org/10.3390/risks8010022 - 01 Mar 2020

Cited by 16 | Viewed by 8359

Abstract

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a [...] Read more.

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance. Full article

(This article belongs to the Special Issue Machine Learning in Insurance)

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79 pages, 1797 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies

by Anne-Sophie Krah, Zoran Nikolić and Ralf Korn

Risks 2020, 8(1), 21; https://doi.org/10.3390/risks8010021 - 21 Feb 2020

Cited by 10 | Viewed by 5549

Abstract

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, [...] Read more.

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests. Full article

(This article belongs to the Special Issue Machine Learning in Insurance)

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