Risks

20 pages, 1176 KiB

Open AccessFeature PaperReview

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

by Michel Dacorogna

Risks 2023, 11(5), 98; https://doi.org/10.3390/risks11050098 - 18 May 2023

Cited by 2 | Viewed by 1478

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, [...] Read more.

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

► Show Figures

Figure 1

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 1583

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)

► Show Figures

Figure 1

17 pages, 3623 KiB

Open AccessArticle

A Non-Performing Loans (NPLs) Portfolio Pricing Model Based on Recovery Performance: The Case of Greece

by Alexandra Z. Marouli, Eugenia N. Giannini and Yannis D. Caloghirou

Risks 2023, 11(5), 96; https://doi.org/10.3390/risks11050096 - 18 May 2023

Viewed by 2487

Abstract

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the [...] Read more.

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the NPL portfolios considering the debtors’ behavioral response to various legal measures (phone calls, extrajudicial notices, court orders, and foreclosures). The model considered the recovery distribution over time and was applied successfully to the case of Greece. The model was also used to predict recovery, cost, and profit future cash flows, and to optimize the collection strategies related to the activation periods of different measures. A sensitivity analysis was also conducted to reveal the most significant factors affecting the collection process. Full article

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

► Show Figures

Figure 1

11 pages, 576 KiB

Open AccessArticle

Prospect Theory and the Favorite Long-Shot Bias in Baseball

by James Nutaro

Risks 2023, 11(5), 95; https://doi.org/10.3390/risks11050095 - 17 May 2023

Viewed by 1139

Abstract

We provide new evidence of a favorite long-shot bias for bets placed on baseball games. Our analysis uses the difference of mean run differentials as an observable proxy for the probability of a team to win. When baseball is viewed through this proxy, [...] Read more.

We provide new evidence of a favorite long-shot bias for bets placed on baseball games. Our analysis uses the difference of mean run differentials as an observable proxy for the probability of a team to win. When baseball is viewed through this proxy, we see that bettors believe favorites are less likely to win than they actually are and long-shots more likely. This result is consistent with prospect theory, which suggests that large and small probabilities are poorly estimated when making decisions with risk. Full article

► Show Figures

Figure 1

27 pages, 7408 KiB

Open AccessArticle

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

by Indranil Ghosh, Esteban Alfaro-Cortés, Matías Gámez and Noelia García-Rubio

Risks 2023, 11(5), 94; https://doi.org/10.3390/risks11050094 - 16 May 2023

Cited by 3 | Viewed by 1429

Abstract

Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt [...] Read more.

Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The present research attempts to propound a robust framework capable of channeling macroeconomic reflectors and essential media chatter-linked variables to draw precise forecasts of future figures for Spanish and Indian stock markets. The predictive structure combines Isometric Mapping (ISOMAP), which is a non-linear feature transformation tool, and Gradient Boosting Regression (GBR), which is an ensemble machine learning technique to perform predictive modelling. The Explainable Artificial Intelligence (XAI) is used to interpret the black-box type predictive model to infer meaningful insights. The overall results duly justify the incorporation of local and global media chatter indices in explaining the dynamics of respective financial markets. The findings imply marginally better predictability of Indian stock markets than their Spanish counterparts. The current work strives to compare and contrast the reaction of developed and developing financial markets during the COVID-19 pandemic, which has been argued to share a close resemblance to the Black Swan event when applying a robust research framework. The insights linked to the dependence of stock markets on macroeconomic indicators can be leveraged for policy formulations for augmenting household finance. Full article

► Show Figures

Figure 1

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 3076

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)

► Show Figures

Figure 1

36 pages, 1366 KiB

Open AccessArticle

Risk Mitigation in Agriculture in Support of COVID-19 Crisis Management

by Boris M. Leybert, Oksana V. Shmaliy, Zhanna V. Gornostaeva and Daria D. Mironova

Risks 2023, 11(5), 92; https://doi.org/10.3390/risks11050092 - 15 May 2023

Cited by 1 | Viewed by 1274

Abstract

The main focus of this article is the problem of exacerbating agricultural risks in the context of the COVID-19 crisis, which started against the background of the novel coronavirus (COVID-19) pandemic. The motivation for conducting the research presented in this article was the [...] Read more.

The main focus of this article is the problem of exacerbating agricultural risks in the context of the COVID-19 crisis, which started against the background of the novel coronavirus (COVID-19) pandemic. The motivation for conducting the research presented in this article was the desire to increase the resilience of agricultural companies to economic crises. This paper is aimed at studying the Russian experience of changing the production and financial risks of agricultural companies during the COVID-19 crisis, substantiating the important role of innovations in reducing these risks, and determining the prospects for risk management in agriculture based on innovations to increase its crisis resilience. Using the structural equation modelling (SEM) method, we modelled the contribution of innovations to the risk management of agriculture during the COVID-19 crisis. The advantages of the SEM method, compared to other conventional methods (e.g., independent correlation analysis or independent regression analysis), include the increased depth of analysis, its systemic character, and the consideration of multilateral connections between the indicators. Using the case-study method, a “smart” vertical farm framework is being developed, the risks of which are resistant to crises through the use of datasets and machine learning. The originality of this article lies in rethinking the risks of agriculture from the standpoint of “smart” technologies as a new risk factor and a way to increase resilience to crises. The theoretical significance of the results obtained is that they make it possible to systematically study the changes in the risks of agriculture in the context of the COVID-19 crisis, while outlining the prospects for increasing resilience to crises based on optimising the use of “smart” technologies. The practical significance of the article is related to the fact that the authors’ conclusions and applied recommendations on the use of datasets and machine learning by agricultural companies can improve the efficiency of agricultural risk management and ensure successful COVID-19 crisis management by agricultural companies. Full article

(This article belongs to the Special Issue The COVID-19 Crisis: Datasets and Data Analysis to Reduce Risks)

► Show Figures

Figure 1

16 pages, 819 KiB

Open AccessReview

A Survey on AI Implementation in Finance, (Cyber) Insurance and Financial Controlling

by Aleksandrina Aleksandrova, Valentina Ninova and Zhelyo Zhelev

Risks 2023, 11(5), 91; https://doi.org/10.3390/risks11050091 - 11 May 2023

Cited by 1 | Viewed by 3051

Abstract

Artificial intelligence is changing the world in unprecedented ways and redefining all areas of human activity. In recent decades, the development of AI has progressed at an extraordinary pace. This study examines the scope of implementing AI in the financial sector, insurance, and [...] Read more.

Artificial intelligence is changing the world in unprecedented ways and redefining all areas of human activity. In recent decades, the development of AI has progressed at an extraordinary pace. This study examines the scope of implementing AI in the financial sector, insurance, and financial controlling. The research team focuses on these areas, as the main objective of this review is to provide a comprehensive walk-through and to fill the gaps in the literature related to AI implementation in finance, insurance, and financial control from an economic perspective. We provide a comprehensive overview of AI implementation in finance, insurance, and financial controlling, highlighting crucial issues in that process and identifying the relationship between the development of these economic sectors and AI. The authors’ team identifies the trends and main themes in the existing literature in AI-related publications in finance, insurance, and financial control. We discuss the main advantages and disadvantages of AI implementation, identified by our research, and also make some suggestions regarding future research having in mind the interdisciplinary of the topic, the vast development of AI and technologies, and the increasing demand for AI-based solutions, services and products. Full article

► Show Figures

Figure 1

13 pages, 788 KiB

Open AccessArticle

A Compound Up-and-In Call like Option for Wind Projects Pricing

by Michele Bufalo, Antonio Di Bari and Giovanni Villani

Risks 2023, 11(5), 90; https://doi.org/10.3390/risks11050090 - 11 May 2023

Viewed by 1187

Abstract

Wind energy projects represent, currently, a valid opportunity to support United Nations Sustainable Development Goal 7. However, these projects can appear financially unattractive considering the unfavorable meteorological conditions, uncertain electricity market price, uncertain market demand, unpredictable project performance, riskiness of investment stages, etc. [...] Read more.

Wind energy projects represent, currently, a valid opportunity to support United Nations Sustainable Development Goal 7. However, these projects can appear financially unattractive considering the unfavorable meteorological conditions, uncertain electricity market price, uncertain market demand, unpredictable project performance, riskiness of investment stages, etc. This paper provides a real options pricing model applied for the evaluation of a wind farm project to include the uncertainty that can affect future performance. The methodology proposed uses a compound call option model with two barriers applied, respectively, to the twofold phase framework that would act as a sort of up-and-in barrier. The compound call option model allows us to valuate the managerial flexibility to proceed with the following investment stages depending on the success of the previous ones and, through the barriers, the methodology gives the investor the opportunity to consider some profitability thresholds below, past which the investment should be abandoned. We develop a discrete case methodology by using the binomial approach. A hypothetical case study is shown to implement the theoretical framework by using likely data. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

► Show Figures

Figure 1

19 pages, 1202 KiB

Open AccessArticle

The COVID-19 Crises: The Threats, Uncertainties and Risks in Entrepreneurial Development

by Nadia Abdelhamid Abdelmegeed Abdelwahed and Bahadur Ali Soomro

Risks 2023, 11(5), 89; https://doi.org/10.3390/risks11050089 - 08 May 2023

Cited by 2 | Viewed by 1719

Abstract

The COVID-19 pandemic and its different waves brought several complications to people’s social lives and massively affected business activities worldwide. Accordingly, in this study, we explored the various COVID-19 threats, uncertainties, and risks that are faced by entrepreneurship, propensity, and development. We applied [...] Read more.

The COVID-19 pandemic and its different waves brought several complications to people’s social lives and massively affected business activities worldwide. Accordingly, in this study, we explored the various COVID-19 threats, uncertainties, and risks that are faced by entrepreneurship, propensity, and development. We applied a deductive approach in this study and utilized cross-sectional data that we collected through a questionnaire. We based this study’s findings on 320 valid cases. By employing structural equation modeling (SEM), we reveal that factors, such as quality of business environment (QoBE) and access to financial resources (AtFR,) have a positive and significant impact on entrepreneurial propensity (EP). On the other hand, the findings reveal that two factors, namely the uncertainties caused by the COVID-19 pandemic (UoCOVID-19) and the risk perceptions of the COVID-19 pandemic (RPoCOVID-19), have a negative effect on EP. This study’s findings provide valuable information about the COVID-19 pandemic and, on particular, on the development of EP among university students. In addition, this study’s findings guide and support policymakers and higher authorities in understanding the impact of the COVID-19 pandemic and other business-related factors for developing EP. Further, these findings support the creation of conducive business environments even during a global pandemic or another natural disaster. Finally, this study’s findings contribute other empirical evidence to enrich previous research on health, business, and management. Full article

(This article belongs to the Special Issue The COVID-19 Crisis: Datasets and Data Analysis to Reduce Risks)

► Show Figures

Figure 1

22 pages, 1590 KiB

Open AccessArticle

Methodology for Environmental Risk Analysis Based on Intuitionistic Fuzzy Values

by Oleg Uzhga-Rebrov and Peter Grabusts

Risks 2023, 11(5), 88; https://doi.org/10.3390/risks11050088 - 04 May 2023

Cited by 3 | Viewed by 1047

Abstract

Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of [...] Read more.

Ecological risks are characterized by a high degree of uncertainty about the chances of unfavorable event outcomes and the losses associated with those outcomes. Subjective expert judgment is widely used when baseline data are insufficient. This introduces additional uncertainties in the results of risk analyses. In order to successfully model the existing uncertainties, this paper presents a methodology for ecological risk analysis that is based on input evaluations in the form of intuitionistic fuzzy values (IFVs). The advantage of this approach is the ability to model a wide range of uncertainties in ecological risk analysis tasks. Full article

► Show Figures

Figure 1

16 pages, 430 KiB

Open AccessArticle

Anticipating the Unforeseen and Expecting the Unexpected: Effectiveness of Macro-Prudential Policies in Curbing the Impact of Stranded Assets in the Banking Sector

by Chekani Nkwaira and Huibrecht Margaretha Van der Poll

Risks 2023, 11(5), 87; https://doi.org/10.3390/risks11050087 - 04 May 2023

Viewed by 1270

Abstract

Banks are exposed to climate risks through stranded assets. This risk can be substantial in the banking sector, as it can spawn systemic risk. After the Great Recession, macro-prudential instruments effectively addressed systemic risk. However, climatic risks raise the research question of how [...] Read more.

Banks are exposed to climate risks through stranded assets. This risk can be substantial in the banking sector, as it can spawn systemic risk. After the Great Recession, macro-prudential instruments effectively addressed systemic risk. However, climatic risks raise the research question of how feasible it is to address them by adopting macro-prudential instruments. The researchers, therefore, investigate how banks can respond to the risk posed by stranded assets through the framework of using macro-prudential instruments. A semi-systematic review of the related literature is carried out based on the researchers’ aim to evaluate theory evidence in the effectiveness of macro-prudential instruments in addressing climate-related risks. The adaptability of macro-prudential instruments to address climatic risks and, by implication, systemic risk is demonstrated in the findings. The researchers develop a framework constituting climate transparency disclosures, climate capital requirement ratio, climate capital conservation, carbon countercyclical buffer and macro-prudential climate stress tests to mitigate the effects of climate risks in banking. Full article

18 pages, 5413 KiB

Open AccessArticle

BeVIXed: Trading Fear in the Volatility Complex

by Chakravarthy Varadarajan and Klaus R. Schenk-Hoppé

Risks 2023, 11(5), 86; https://doi.org/10.3390/risks11050086 - 04 May 2023

Viewed by 2098

Abstract

We explain the evolution of the volatility market and present the infamous day of ‘Volmageddon’ as an insightful case study. Our survey focuses on the pricing and trading of volatility-linked assets, highlighting the impact of mechanical hedging in markets for futures and higher-order [...] Read more.

We explain the evolution of the volatility market and present the infamous day of ‘Volmageddon’ as an insightful case study. Our survey focuses on the pricing and trading of volatility-linked assets, highlighting the impact of mechanical hedging in markets for futures and higher-order derivatives. We supplement the vast statistical analysis of volatility derivatives with a financial economist’s perspective. Full article

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

► Show Figures

Figure 1

18 pages, 2313 KiB

Open AccessArticle

Pricing Kernels and Risk Premia implied in Bitcoin Options

by Julian Winkel and Wolfgang Karl Härdle

Risks 2023, 11(5), 85; https://doi.org/10.3390/risks11050085 - 30 Apr 2023

Viewed by 1431

Abstract

Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor [...] Read more.

Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class. Full article

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

► Show Figures

Figure 1

15 pages, 432 KiB

Open AccessArticle

Assessing the Causality Relationship between the Geopolitical Risk Index and the Agricultural Commodity Markets

by Joseph Micallef, Simon Grima, Jonathan Spiteri and Ramona Rupeika-Apoga

Risks 2023, 11(5), 84; https://doi.org/10.3390/risks11050084 - 30 Apr 2023

Cited by 3 | Viewed by 2045

Abstract

The aim of this study was to investigate the Granger causality between geopolitical risk (GPR) sub-indices in order to examine the implications of geopolitical risk on ten agricultural commodities classified as softs or grains. The Granger causality test was used to determine the [...] Read more.

The aim of this study was to investigate the Granger causality between geopolitical risk (GPR) sub-indices in order to examine the implications of geopolitical risk on ten agricultural commodities classified as softs or grains. The Granger causality test was used to determine the causal relationship between the daily GPR sub-indices and the future prices of ten essential agricultural commodities from 31 March 2000 to 31 March 2022. We discovered that the GPR Threat and Act sub-indices Granger-caused changes in the wheat and oat commodity prices. These findings were also connected to the ongoing Russian–Ukrainian conflict, which has had an impact on agricultural commodity prices because both countries are major agricultural producers. The empirical results also showed how the GPR Threat sub-index Granger-affected the future prices of soybean oil, coffee, wheat, and oats. On the other hand, the GPR Act sub-index only Granger-affected the future price of oats. The findings of this study should provide useful information to both policymakers and governments to help them acknowledge the importance of geopolitical risk when setting their national policies related to food security. Full article

24 pages, 695 KiB

Open AccessArticle

Sparse Modeling Approach to the Arbitrage-Free Interpolation of Plain-Vanilla Option Prices and Implied Volatilities

by Daniel Guterding

Risks 2023, 11(5), 83; https://doi.org/10.3390/risks11050083 - 28 Apr 2023

Viewed by 1385

Abstract

We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal density, we write these integral equations [...] Read more.

We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal density, we write these integral equations as a system of linear equations. We show that the kernel matrix of this system is, in general, ill-conditioned, so that it cannot be solved for the discretized density using a naive approach. Instead, we construct a sparse model for the kernel matrix using singular value decomposition (SVD), which allows us not only to systematically improve the condition number of the kernel matrix, but also determines the computational effort and accuracy of our method. In order to allow for the treatment of realistic inputs that may contain arbitrage, we reformulate the system of linear equations as an optimization problem, in which the SVD-transformed density minimizes the error between the input prices and the arbitrage-free prices generated by our method. To further stabilize the method in the presence of noisy input prices or arbitrage, we apply an

L_{1}

-regularization to the SVD-transformed density. Our approach, which is inspired by recent progress in theoretical physics, offers a flexible and efficient framework for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, without the need to explicitly specify a stochastic process, expansion basis functions or any other kind of model. We demonstrate the capabilities of our method in a number of artificial and realistic test cases. Full article

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

► Show Figures

Figure 1

20 pages, 2904 KiB

Open AccessArticle

Developing a System for Monitoring Human Resource Risks in a Digital Economy

by Ivan Babkin, Valentina Pulyaeva, Irina Ivanova, Yulya Veys and Guljakhon Makhmudova

Risks 2023, 11(5), 82; https://doi.org/10.3390/risks11050082 - 27 Apr 2023

Cited by 1 | Viewed by 1728

Abstract

Human resource (HR) risks are significant negative aspects of any organization. The main problem in the theory and practice of modern organizations is that there is no complex model and algorithm for managing HR risks. To define the essence of HR risks and [...] Read more.

Human resource (HR) risks are significant negative aspects of any organization. The main problem in the theory and practice of modern organizations is that there is no complex model and algorithm for managing HR risks. To define the essence of HR risks and basic approaches to their management, the authors conducted a survey of employees concerning the HR sphere. The authors used cluster and correlation–regression analysis to process the results of the survey conducted among employees about HR risks. Relying on general scientific research methods, data from open sources, including the review of scientific papers of foreign and national researchers and practitioners, and considering the opinions of the sociological survey respondents, the authors concluded that there is a need for carrying out close work with personnel to prevent conflicts in the working environment, increase the motivation for work, and involve the management team in regulating labor relationships. The scientific novelty of the study is that it considers the process of managing HR risks from a systemic perspective, while they are monitored based on the conceptual model suggested in the study. The models developed by the authors can be used in reality for managing HR risks faced by economic entities. Full article

(This article belongs to the Special Issue Risk Analysis and Management in the Digital and Innovation Economy)

► Show Figures

Figure 1

32 pages, 693 KiB

Open AccessArticle

Economic Consequences of Greylisting by the Financial Action Task Force

by Louis de Koker, John Howell and Nicholas Morris

Risks 2023, 11(5), 81; https://doi.org/10.3390/risks11050081 - 25 Apr 2023

Viewed by 9419

Abstract

This study considers the impact of the FATF’s greylisting process from a market perspective. The results are intended to inform the development of public policy and improvement of market signalling. The study develops a theoretical market impact model and identifies the indicators which [...] Read more.

This study considers the impact of the FATF’s greylisting process from a market perspective. The results are intended to inform the development of public policy and improvement of market signalling. The study develops a theoretical market impact model and identifies the indicators which may impact banking operations and institutional decisions. It is explicitly market-oriented in that the model seeks to reflect how stakeholders in financial and non-financial markets typically respond to signals sent out by the FATF. The authors find that the FATF’s greylisting signalling has changed over time and distinguish four broad periods reflecting different messaging. The study uses financial, trade, and other variables derived from the World Bank’s ‘World Development Indicators’ databank to explore evidence of impact in each of the different phases of the FATF’s approach to greylisting. The study uses a pooled cross-section and time series approach with fixed effects, based on a sample of 177 countries and 3540 country-years of data from 2000 to 2020. The study examines impacts on net official development assistance, the banking environment (non-performing loans, risk premiums), net foreign assets, indebtedness, and market capitalisation. It finds significant correlations between many financial variables and FATF listing events, including an apparent reduction in development assistance during greylisting periods which endured after the country was delisted. This is of significant concern, as such reductions may impact disproportionately on developing economies. Full article

► Show Figures

Figure 1

3 pages, 277 KiB

Open AccessEditorial

Special Issue “Data Science in Insurance”

by Gian Paolo Clemente, Francesco Della Corte, Nino Savelli and Diego Zappa

Risks 2023, 11(5), 80; https://doi.org/10.3390/risks11050080 - 24 Apr 2023

Viewed by 1424

Abstract

Within the insurance field, the digital revolution has enabled the collection and storage of large quantities of information [...] Full article

(This article belongs to the Special Issue Data Science in Insurance)

26 pages, 1195 KiB

Open AccessArticle

Estimating the Value-at-Risk by Temporal VAE

by Robert Buch, Stefanie Grimm, Ralf Korn and Ivo Richert

Risks 2023, 11(5), 79; https://doi.org/10.3390/risks11050079 - 23 Apr 2023

Cited by 2 | Viewed by 1924

Abstract

Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. As the joint log-returns of asset prices can often be projected to a latent space of a much smaller dimension, the use of a variational [...] Read more.

Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. As the joint log-returns of asset prices can often be projected to a latent space of a much smaller dimension, the use of a variational autoencoder (VAE) for estimating the VaR is a natural suggestion. To ensure the bottleneck structure of autoencoders when learning sequential data, we use a temporal VAE (TempVAE) that avoids the use of an autoregressive structure for the observation variables. However, the low signal-to-noise ratio of financial data in combination with the auto-pruning property of a VAE typically makes use of a VAE prone to posterior collapse. Therefore, we use annealing of the regularization to mitigate this effect. As a result, the auto-pruning of the TempVAE works properly, which also leads to excellent estimation results for the VaR that beat classical GARCH-type, multivariate versions of GARCH and historical simulation approaches when applied to real data. Full article

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

► Show Figures

Figure 1

Journal Menu

Journal Browser

Risks, Volume 11, Issue 5 (May 2023) – 20 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI