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A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 54356

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Prof. Dr. Krzysztof Jajuga

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Department of Financial Investments and Risk Management, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland
Interests: multivariate data analysis; classification; econometrics; financial markets; risk management; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Józef Dziechciarz

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Guest Editor

Department of Econometrics, Wroclaw University of Economics and Business, ul. Komandorska 118/120, 53-345 Wroclaw, Poland
Interests: econometrics; data analysis; marketing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit papers to be published in the Special Issue “Data Analysis for Risk Management – Economics, Finance and Business”. The main motivation for this volume is to provide recent results of the research in the area of data analysis to be applied in widely understood risk management.

We welcome papers which address two main directions that have been substantially explored in last decade. The first is methodological development, leading to new proposals in classical multivariate data analysis and in the machine learning area. The second is the development in new types of data (in addition to numerical data), with new added opportunities in risk management through the exploration of alternative data such as symbolic data, text data, and spatial data, among other examples.

This Special Issue will contain both methodological and empirical papers. We encourage sharing the results of research based not only on data from economics, finance, and business, but – given the multidisciplinary approach – also on data from related areas such as social or natural sciences, since they can have an impact on economics, finance, or business.

Such a mix of theory and applications will add value for both scholars and practitioners in the various disciplines of science.

Prof. Dr. Krzysztof Jajuga
Prof. Dr. Józef Dziechciarz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

multivariate data analysis
classification and clustering
machine learning methods
natural language processing
risk management
financial data
macro- and microeconomic data

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Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 275 KiB

Open AccessEditorial

Data Analysis for Risk Management—Economics, Finance and Business: New Developments and Challenges

by Krzysztof Jajuga

Risks 2023, 11(4), 70; https://doi.org/10.3390/risks11040070 - 30 Mar 2023

Viewed by 2733

Abstract

The development of the theory and practice of risk management is closely related to the emergence of different risks [...] Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

Research

Jump to: Editorial

17 pages, 1393 KiB

Open AccessArticle

Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data

by Błażej Kochański

Risks 2022, 10(10), 184; https://doi.org/10.3390/risks10100184 - 20 Sep 2022

Cited by 3 | Viewed by 3220

Abstract

In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the credit-scoring context. The model list includes the binormal, bigamma, bibeta, bilogistic, power, and bifractal curves. The models are then tested against empirical credit-scoring ROC data from publicly available presentations and papers, as well as from European retail lending institutions. Except for the power curve, all the presented models fit the data quite well. However, based on the results and other favourable properties, it is suggested that the binormal curve is the preferred choice for modelling credit-scoring ROC curves. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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14 pages, 2645 KiB

Open AccessArticle

An Efficient Method for Pricing Analysis Based on Neural Networks

by Yaser Ahmad Arabyat, Ahmad Ali AlZubi, Dyala M. Aldebei and Samerra’a Ziad Al-oqaily

Risks 2022, 10(8), 151; https://doi.org/10.3390/risks10080151 - 28 Jul 2022

Cited by 3 | Viewed by 2163

Abstract

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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33 pages, 1281 KiB

Open AccessArticle

An Overview on the Landscape of R Packages for Open Source Scorecard Modelling

by Gero Szepannek

Risks 2022, 10(3), 67; https://doi.org/10.3390/risks10030067 - 18 Mar 2022

Cited by 3 | Viewed by 4538

Abstract

The credit scoring industry has a long tradition of using statistical models for loan default probability prediction. Since this time methodology has strongly evolved, and most of the current research is dedicated to modern machine learning algorithms which contrasts with common practice in the finance industry where traditional regression models still denote the gold standard. In addition, strong emphasis is put on a preliminary binning of variables. Reasons for this may be not only the regulatory requirement of model comprehensiveness but also the possibility to integrate analysts’ expert knowledge in the modelling process. Although several commercial software companies offer specific solutions for modelling credit scorecards, open-source frameworks for this purpose have been missing for a long time. In recent years, this has changed, and today several R packages for credit scorecard modelling are available. This brings the potential to bridge the gap between academic research and industrial practice. The aim of this paper is to give a structured overview of these packages. It may guide users to select the appropriate functions for the desired purpose. Furthermore, this paper will hopefully contribute to future development activities. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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27 pages, 2173 KiB

Open AccessArticle

Measurement of Systemic Risk in the Colombian Banking Sector

by Orlando Rivera-Escobar, John Willmer Escobar and Diego Fernando Manotas

Risks 2022, 10(1), 22; https://doi.org/10.3390/risks10010022 - 13 Jan 2022

Cited by 4 | Viewed by 3777

Abstract

This paper uses three methodologies for measuring the existence of systemic risk in the Colombian banking system. The determination of its existence is based on implementing three systemic risk measures widely referenced in academic works after the subprime crisis, known as CoVaR, MES and SRISK. Together, the three methodologies were implemented for the case of Colombian Banks during the 2008–2017 period. The findings allow us to establish that the Colombian banking sector did not present a systemic risk scenario, despite having suffered economic losses due to external shocks, mainly due to the subprime crisis. The results and findings show the efficiency of the systemic risk measures implemented in this study as an alternative to measure systemic risk in banking systems. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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25 pages, 5186 KiB

Open AccessArticle

Volatility Modeling and Dependence Structure of ESG and Conventional Investments

by Joanna Górka and Katarzyna Kuziak

Risks 2022, 10(1), 20; https://doi.org/10.3390/risks10010020 - 12 Jan 2022

Cited by 11 | Viewed by 5092

Abstract

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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16 pages, 3465 KiB

Open AccessArticle

New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach

by Aneta Ptak-Chmielewska and Paweł Kopciuszewski

Risks 2022, 10(1), 16; https://doi.org/10.3390/risks10010016 - 9 Jan 2022

Cited by 2 | Viewed by 6984

Abstract

After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the validity of currently used risk models and processes. The recommendation presented in this paper is to address current gaps by considering a Bayesian approach for PD recalibration based on insights derived from both simulated and empirical data (e.g., a priori and a posteriori distributions). A Bayesian approach was used in two steps: to calculate the Long Run Average (LRA) on both simulated and empirical data and for the final model calibration to the posterior LRA. The Bayesian approach result for the PD LRA was slightly lower than the one calculated based on classical logistic regression. It also decreased for the historically observed LRA that included the most recent empirical data. The Bayesian methodology was used to make the LRA more objective, but it also helps to better align the LRA not only with the empirical data but also with the most recent ones. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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24 pages, 5051 KiB

Open AccessArticle

Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data

by Tomasz Berent and Radosław Rejman

Risks 2021, 9(12), 217; https://doi.org/10.3390/risks9120217 - 2 Dec 2021

Cited by 2 | Viewed by 3162

Abstract

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any scoring device to discriminate between healthy companies and potential defaulters, we model default probability using a doubly stochastic Poisson process. Our paper is unique in that it uses a large dataset of non-public companies with low-quality reporting standards and very patchy data. We believe this is the first attempt to apply the Duffie–Duan formulation to emerging markets at such a scale. Our results are comparable, if not more robust, than those obtained for public companies in developed countries. The out-of-sample accuracy ratios range from 85% to 76%, one and three years prior to default, respectively. What we lose in (data) quality, we regain in (data) quantity; the power of our tests benefits from the size of the sample: 15,122 non-financial companies from 2007 to 2017, unique in this research area. Our results are also robust to model specification (with different macro and company-specific covariates used) and statistically significant at the 1% level. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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19 pages, 1778 KiB

Open AccessArticle

Risk Factors Affecting Bancassurance Development in Poland

by Adam Śliwiński, Joanna Dropia and Norbert Duczkowski

Risks 2021, 9(7), 130; https://doi.org/10.3390/risks9070130 - 7 Jul 2021

Cited by 3 | Viewed by 4176

Abstract

The aim of the article is to identify the risk factors affecting bancassurance development in Poland. The development is understood here as a change of gross written premiums obtained through banks in Poland. The group of risk factors selected in a survey conducted among financial sector employees was subject to statistical verification. The analysis used both variables directly related to the insurance product (e.g., a regulatory restriction of insurance acquisition costs) as well as those resulting from the specificity of the bancassurance channel, such as the sales of banking products, i.e., cash loans, housing loans and the value of funds placed by customers on deposits. The study was conducted on the basis of data on the gross premiums written in Poland in the years 2004–2019. The result of the applied model confirms the assumptions and the importance of insurance distribution in banks. Significant risk factors (statistically significant) which determine gross premiums written in the bancassurance channel are: the size of policyholder’s family (number of children, dependants) represented by the average number of people in a household in Poland, demand on mortgage loans represents by bank housing loans for households and agent’s commission, represented by the ratio of acquisition costs to gross written premium. The results of the econometric model obtained are consistent with expectations arising from the principles and practice of cooperation between banks and insurers as well as the specificity of insurance products distribution (also local) in the bancassurance channel. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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29 pages, 5306 KiB

Open AccessArticle

Systemic Illiquidity Noise-Based Measure—A Solution for Systemic Liquidity Monitoring in Frontier and Emerging Markets

by Ewa Dziwok and Marta A. Karaś

Risks 2021, 9(7), 124; https://doi.org/10.3390/risks9070124 - 1 Jul 2021

Cited by 5 | Viewed by 3010

Abstract

The paper presents an alternative approach to measuring systemic illiquidity applicable to countries with frontier and emerging financial markets, where other existing methods are not applicable. We develop a novel Systemic Illiquidity Noise (SIN)-based measure, using the Nelson–Siegel–Svensson methodology in which we utilize the curve-fitting error as an indicator of financial system illiquidity. We empirically apply our method to a set of 10 divergent Central and Eastern Europe countries—Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, and Slovakia—in the period of 2006–2020. The results show three periods of increased risk in the sample period: the global financial crisis, the European public debt crisis, and the COVID-19 pandemic. They also allow us to identify three divergent sets of countries with different systemic liquidity risk characteristics. The analysis also illustrates the impact of the introduction of the euro on systemic illiquidity risk. The proposed methodology may be of consequence for financial system regulators and macroprudential bodies: it allows for contemporaneous monitoring of discussed risk at a minimal cost using well-known models and easily accessible data. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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23 pages, 1020 KiB

Open AccessArticle

Risk Management and Financial Stability in the Polish Public Hospitals: The Moderating Effect of the Stakeholders’ Engagement in the Decision-Making

by Aldona Frączkiewicz-Wronka, Tomasz Ingram, Karolina Szymaniec-Mlicka and Piotr Tworek

Risks 2021, 9(5), 87; https://doi.org/10.3390/risks9050087 - 6 May 2021

Cited by 6 | Viewed by 6569

Abstract

Public healthcare organizations usually operate under significant financial strain and frequently strive for survival. Thus, in most cases, financial stability is a “holy grail” of public healthcare organizations in general and hospitals in particular. The financial stability itself is partly dependent upon the ability to manage risk associated with hospital actions. In the paper, we seek to address the question related to the moderating role of stakeholders’ engagement in the relationship between risk management practices and a hospital’s financial stability. To answer this question, we designed and carried out empirical research on a sample of 103 out of 274 Polish public hospitals operating at the first-level (closest to the patient). Results show that risk management practices are positively related to financial stability. Hospitals with well-developed risk management practices are better prepared and find appropriate answers to threats, helping them attain financial stability. We also found that stakeholder engagement acts as a moderator of the relationship between risk management practices and financial stability. Research results indicate that with more sophisticated risk management practices, stakeholder engagement in decision-making leads to statistically lower financial stability. On the other hand, high levels of stakeholders’ engagement help when risk management practices are underdeveloped. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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19 pages, 756 KiB

Open AccessArticle

Exchange Rate Volatility, Currency Misalignment, and Risk of Recession in the Central and Eastern European Countries

by Victor Shevchuk and Roman Kopych

Risks 2021, 9(5), 82; https://doi.org/10.3390/risks9050082 - 1 May 2021

Cited by 4 | Viewed by 4876

Abstract

This study is aimed at estimation of the exchange rate volatility and its impact on the business cycle fluctuations in four central and eastern European countries (the Czech Republic, Hungary, Poland, and Romania). Exchange rate volatility is estimated with the EGARCH(1,1) model. It is found that exchange rate volatility is affected by the components of the Index of Economic Freedom from the Heritage Foundation, besides inflation and crisis developments. The empirical results using GMM estimation technique and comprehensive robustness checks suggest that exchange rate volatility reduces the risk of recession in the Czech Republic while the opposite effect is found for Hungary and Romania, with a neutrality for Poland. These findings continue to hold after controlling for the fiscal and monetary policy indicators. There is evidence that the RER undervaluation prevents sliding into a recession on a credible basis in Poland only, with a neutral stance for other countries. Except in Romania, higher levels of economic freedom is associated with worsening of the cyclical position of output. Among other results, stabilization policies in the recession imply fiscal tightening for the Czech Republic and Romania, higher money supply for the Czech Republic and Poland, and lower central bank reference rate for Hungary. Full article

(This article belongs to the Special Issue Data Analysis for Risk Management – Economics, Finance and Business)

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