Special Issue "From COVID-19 to Resilience: Quantitative Methods in Economics and Business"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 November 2022.

Special Issue Editors

Dr. Lina Novickytė
E-Mail Website
Guest Editor
Government Strategic Analysis Center, A. Gostauto str. 9, 2F, LT-01108 Vilnius, Lithuania
Interests: multiple-criteria decision-making (MCDM) analysis; efficiency; banks; data envelopment analysis; input–output; non-parametric methods; behavioral economics; risk management; behavioral finance
Dr. Jolanta Drozdz
E-Mail Website
Guest Editor
Faculty of Economics and Business Administration, Vilnius University, Sauletekio ave. 9, II bld., LT-01513 Vilnius, Lithuania
Interests: risk management; agricultural finance; agricultural policy; international economics; international trade; foreign direct investments
Prof. Dr. Radosław Pastusiak
E-Mail Website
Guest Editor
Faculty of Economics and Sociology, University of Lodz, POW Street 3/5, 90-255 Lodz, Poland
Interests: capital markets; corporate finance; agricultural finance; sustainability
Dr. Michał Soliwoda
E-Mail Website
Guest Editor
Faculty of Economics and Sociology, University of Lodz, POW Street 3/5, 90-255 Lodz, Poland
Interests: risk management household finance; SME finance; agricultural finance; agricultural policy

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic represents a particularly important challenge for empirical research in economics and finance. The “resilience” category is often used in international documents concerning crisis management. The category of resilience enters numerous, usually overly complex interactions with many important socio-economic and public categories, important for various spheres of the economy, politics, and life of citizens. The aim of the Special Issue is to explore quantitative methods of measuring resilience, in the context of the COVID-19 pandemic. The measurement and assessment of resilience at different levels requires advanced quantitative and qualitative methods that are based on dynamic approaches and interaction with other socio-economic categories, which, consequently, makes it exceedingly difficult to develop public policies that are oriented towards strengthening resilience in general. A good example is the integrated measurement and assessment of economic and financial resilience of households by international organisations. There is a possible conflict in macro and micro approaches, which can be greatly beneficial at the application level to check the consistency of assumptions, approaches, and conclusions. In the context of public policy, the concept of resilience enables a more in-depth understanding of how economic entities cope with shocks and tensions.

This Special Issue aims at collecting original research papers, comprehensive reviews on the theory and practice on development and implementation of advanced mathematical and instrumental methods in the field of assessing resilience in various economic and business sectors. Applied quantitative methods will allow a better understanding of the interactions between economic and business socio-economic activities, and will contribute to the development of new models or the improvement of existing ones. These methods can be based on traditional modelling and forecasting tools, as well as on the new tools associated with the development of intelligent digital technologies, big data use, and so on.

Dr. Lina Novickytė
Dr. Jolanta Drozdz
Prof. Dr. Radosław Pastusiak
Dr. Michał Soliwoda
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 papers will be 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. Mathematics is an international peer-reviewed open access semimonthly 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

  • economic mathematics
  • business mathematics
  • financial mathematics
  • digital economy
  • risk management
  • economic modelling and forecasting
  • managerial economics
  • resilience
  • quantitative methods
  • decision-making
  • behavioral change
  • practical applications (government, economics, development, agriculture, industry, services)

Published Papers (3 papers)

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Research

Article
Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model
Mathematics 2021, 9(23), 3087; https://0-doi-org.brum.beds.ac.uk/10.3390/math9233087 - 30 Nov 2021
Viewed by 292
Abstract
Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, [...] Read more.
Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models. Full article
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Article
Theory and Practice of Quantitative Assessment of System Harmonicity: Case of Road Safety in Russia before and during the COVID-19 Epidemic
Mathematics 2021, 9(21), 2812; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212812 - 05 Nov 2021
Viewed by 455
Abstract
People have had an interest in harmony issues for thousands of years; however, there is still no elaborated system of views on these questions. Ancient Greeks understood harmony as an agreement of opposites. A surge of interest in the study of the harmonic [...] Read more.
People have had an interest in harmony issues for thousands of years; however, there is still no elaborated system of views on these questions. Ancient Greeks understood harmony as an agreement of opposites. A surge of interest in the study of the harmonic aspects of being occurred in the twentieth century due to the development of systems science, particularly regarding synergetic system effects. At the same time, there are still relatively few applications of synergetics because of the absence of an accurate methodology for the identification of system harmonicity. The aim of this research is to develop the methodology for the quantitative assessment of system harmonicity by considering a practical example: the quantitative assessment of the harmonicity of the road safety provision system (RSS) and its dynamics during the last 15 years (2006–2020). In addition, the impact of the COVID restrictions on population mobility in Russia in 2020, on the change in the harmonicity of the road safety provision system, is considered. During the research it was established that the quality factor g of the Russian road safety provision system changed from g2006 = 1.9565 to g2020 = 2.4646, which promoted the decline of the relative entropy of the Russian road safety provision system from Hn RSS2006 = 0.8623 to Hn RSS2020 = 0.7553. The deep reason for that change was the modification of relation between “weights” or the significance of the contribution of different elements of the cause-and-effect chain in the formation of the factual level of the road accident rate in Russia in the last 15 years. The main conclusion of this research is that the harmonicity of the Russian road safety provision system, assessed by the normalized functional general utility GUn, has been increased, and it has already exceeded the level of harmonious reference systems GUn = 0.618. In fact, the normalized functional general utility GUn of the Russian road safety provision system increased from GUnRSS2006 = 0.615 to GUnRSS2020 = 0.652 (by 6.0%), from 2006 to 2020. Simultaneously, the share of the normalized used resource Xn declined, allowing a conclusion to be drawn about a significant improvement in the balance “efficiency-quality” of the Russian road safety provision system. The COVID lockdown played a positive role in this process. Harmonicity of the Russian road safety provision system, assessed by the normalized general utility GUnRSS, increased by 0.46% from 2019 to 2020. Full article
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Article
On K-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts
Mathematics 2021, 9(20), 2639; https://0-doi-org.brum.beds.ac.uk/10.3390/math9202639 - 19 Oct 2021
Viewed by 579
Abstract
The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to [...] Read more.
The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to the perceived degree of tourists’ and customers’ exposure to COVID-19 are deemed relevant for sectoral recovery. Due to the subjectivity of predetermining categories, along with the failure of capturing vagueness and uncertainty in the evaluation process, this work explores the use k-means clustering with dataset values expressed as interval-valued intuitionistic fuzzy sets. In addition, the proposed method allows for the incorporation of criteria (or attribute) weights into the dataset, often not considered in traditional k-means clustering but relevant in clustering problems with attributes having varying priorities. Two previously reported case studies were analyzed to demonstrate the proposed approach, and comparative and sensitivity analyses were performed. Results show that the priorities of the criteria in evaluating tourist sites remain the same. However, in evaluating restaurants, customers put emphasis on the physical characteristics of the restaurants. The proposed approach assigns 12, 15, and eight sites to the “low exposure”, “moderate exposure”, and “high exposure” cluster, respectively, each with distinct characteristics. On the other hand, 16 restaurants are assigned “low exposure”, 16 to “moderate exposure”, and eight to “high exposure” clusters, also with distinct characteristics. The characteristics described in the clusters offer meaningful insights for sectoral recovery efforts. Findings also show that the proposed approach is robust to small parameter changes. Although idiosyncrasies exist in the results of both case studies, considering the characteristics of the resulting clusters, tourists or customers could evaluate any tourist site or restaurant according to their perceived exposure to COVID-19. Full article
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