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Risk Management in Economics and Finance for Sustainable Development in...
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Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 10738

Special Issue Editor

Dr. Mariana Petrova

E-Mail Website
Guest Editor
1. Department of Information Technologies, St. Cyril and St. Methodius University of Veliko Tarnovo, Veliko Tarnovo, Bulgaria
2. Institute for Scientific Research, D.A. Tsenov Academy of Economics, Svishtov, Bulgaria
Interests: business applications; digital solutions in business and management; smart economics and innovation; smart data analysis in economics

Special Issue Information

Dear Colleagues,

The digital sector in the structure of the economy is developing at a high rate, and digitalization is changing the usual financial and economic relations. The digital economy is a new way for the economy to work. The phenomenon of the digital economy is being actively studied by scientists, but a common understanding has not yet been formed. This complicates its development and gives rise to risks. It is necessary to identify and analyze the risks of the digital economy, review the experience of risk management in different countries, and develop a methodology for researching the digital economy. New research on risk modeling in the digital age, comprehensive analyses of digital processing, the impact of digital risks on markets, digital risk management, digital security, and other topics are welcomed. There are several important research areas which are necessary to address, such as trends in the digital economy; the transformation of the economy and society; the development of specific management methods based on digital management technologies; and risk management tools. Moreover, the role of banking management and financial markets has become increasingly important under conditions of high uncertainties and risks for both empirical and theoretical reasons, even more so in the context of extreme events such as the economic crisis generated by the COVID-19 pandemic. Theoretical and empirical articles, large-scale quantitative research based on big data, business reviews, and case studies are welcome.

Dr. Mariana Petrova
Guest Editor

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

  • risk
  • financial risks
  • financial risk management
  • financial modeling
  • financial management
  • financial markets
  • risk analysis
  • risk management
  • digital economy
  • digital banking
  • digital transformation
  • digital solutions
  • challenges in digital environment
  • sustainable economical environment
  • regional sustainability and stability
  • sustainable finance
  • business management
  • investment decisions
  • convolutional neural network (CNN)
  • short-term estimation
  • data set modeling

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Published Papers (4 papers)

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Research

21 pages, 2243 KiB  
Article
Risk Management in the Area of Bitcoin Market Development: Example from the USA
by Laeeq Razzak Janjua, Iza Gigauri, Agnieszka Wójcik-Czerniawska and Elżbieta Pohulak-Żołędowska
Risks 2024, 12(4), 67; https://doi.org/10.3390/risks12040067 - 15 Apr 2024
Viewed by 2159
Abstract
This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January [...] Read more.
This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January 2011 to November 2023 reveals that volatile US economic policy indicators, such as high economic policy uncertainty, volatile inflation, and rising interest rates, have recently exerted a negative impact on Bitcoin returns. This study shows that these results are true not only for traditional money but also for cryptocurrencies such as Bitcoin, despite their cardinal features. Its decentralized nature, indicating that it has no physical representation, is not tied to any authority or national economy and relies on a complex algorithm to track transactions. Further, it yields volatile returns that depend on macroeconomic indicators. Full article
(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
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30 pages, 446 KiB  
Article
Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds
by Mariana Petrova and Teodor Todorov
Risks 2023, 11(11), 197; https://doi.org/10.3390/risks11110197 - 14 Nov 2023
Cited by 2 | Viewed by 1802
Abstract
The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH [...] Read more.
The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund “Golden Lev Index 30” stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the “EF Rapid” investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds. Full article
(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
15 pages, 837 KiB  
Article
Internet of Things and Big Data Analytics for Risk Management in Digital Tourism Ecosystems
by Petya Popova, Kremena Marinova and Veselin Popov
Risks 2023, 11(10), 180; https://doi.org/10.3390/risks11100180 - 18 Oct 2023
Cited by 2 | Viewed by 2362
Abstract
Participation and inclusion in the business ecosystem have emerged as a growing trend for company collaboration in areas such as innovation, product development, and research. Collaborations can take many forms, ranging from the traditional value chain to strategic alliances, corporate networks, and digital [...] Read more.
Participation and inclusion in the business ecosystem have emerged as a growing trend for company collaboration in areas such as innovation, product development, and research. Collaborations can take many forms, ranging from the traditional value chain to strategic alliances, corporate networks, and digital ecosystems. The Internet of Things (IoT) and Big Data Analytics (BDA) play key roles in developing smart tourism destinations by delivering efficient management solutions, increased public safety, and improved operational efficiency while managing different risks and challenges, while also being a source of such risks and challenges. The objective of this article was to investigate the potential of IoT and BDA to properly control the risks associated with participants in a tourism destination’s digital ecosystem. The authors used the systematic literature review (SLR) method to examine scientific and applied articles on this subject. As a result, the main risks of the digital tourism ecosystem (DTE) as a whole and of the IoT and BDA technologies used in it were identified and classified; the features of DTE that affect risk management in it were distinguished; IoT technologies and their applications used in DTE were outlined; and the roles of DTE participants and the possible IoT technologies that can successfully address the risks associated with a given role were defined. Full article
(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
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17 pages, 4296 KiB  
Article
Machine Learning Algorithm for Mid-Term Projection of the EU Member States’ Indebtedness
by Silvia Zarkova, Dimitar Kostov, Petko Angelov, Tsvetan Pavlov and Andrey Zahariev
Risks 2023, 11(4), 71; https://doi.org/10.3390/risks11040071 - 3 Apr 2023
Cited by 4 | Viewed by 2562
Abstract
The main research question addressed in the paper is related to the possibility of medium-term forecasting of the public debts of the EU member states. The analysis focuses on a broad range of indicators (macroeconomic, fiscal, monetary, global, and convergence) that influence the [...] Read more.
The main research question addressed in the paper is related to the possibility of medium-term forecasting of the public debts of the EU member states. The analysis focuses on a broad range of indicators (macroeconomic, fiscal, monetary, global, and convergence) that influence the public debt levels of the EU member states. A machine learning prediction model using random forest regression was approbated with the empirical data. The algorithm was applied in two iterations—a primary iteration with 33 indicators and a secondary iteration with the 8 most significant indicators in terms of their influence and forecasting importance regarding the development of public debt across the EU. The research identifies a change in the medium term (2023–2024) in the group of the four most indebted EU member states, viz., that Spain will be replaced by France, which is an even more systemic economy, and will thus increase the group’s share of the EU’s GDP. The results indicate a logical scenario of rising interest rates with adverse effects for the fiscal imbalances, which will require serious reforms in the public sector of the most indebted EU member states. Full article
(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
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