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Mathematics
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Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences
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Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5411

Special Issue Editors

Dr. Alexey Mikhaylov

E-Mail Website
Guest Editor
Financial University under the Government of the Russian Federation, Moscow, Russia
Interests: machine learning; sustainability; metal composites; financial markets; energy; innovation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikita Moiseev

E-Mail Website
Guest Editor
Financial University under the Government of the Russian Federation, Moscow, Russia
Interests: energy economics; econometrics; financial economics

Special Issue Information

Dear Colleagues,

In the era of big data, abundant statistics and unprecedented computer power, we are still facing numerous problems concerning data processing, model specification, model selection, algorithm efficiency, etc., when trying to make predictions or understand the nature of certain economic or social processes. Such classes of datasets often have time-varying parameters, making it difficult to build adequate models, even if the datasets are large.

The purpose of this Special Issue is to contribute to the elaboration of new methods and statistical tools that would help researchers to model economic and social environments more efficiently. Therefore, we are looking for articles that propose novel mathematical tools and methods that can adequately describe and provide insight into the core of socioeconomic processes. The articles are expected to cover a wide range of topics, such as social statistics, financial modelling, statistics in marketing, macroeconomic time-series forecasting, panel data, cross-country economic modelling and regional econometrics.

Dr. Alexey Mikhaylov
Prof. Dr. Nikita Moiseev
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. 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 2600 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

  • Regression analysis
  • Econometric modelling
  • Machine learning
  • Social statistics
  • Time-series forecasting
  • Panel data

Published Papers (2 papers)

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Research

13 pages, 1695 KiB  
Article
Use of Probabilistic Approaches to Predict Cash Deficits
by Ilya Slobodnyak, Anatoly Sidorov and Denis Alekseev
Mathematics 2021, 9(24), 3309; https://doi.org/10.3390/math9243309 - 19 Dec 2021
Viewed by 1829
Abstract
This article deals with issues related to the use of mathematical methods of cash deficit probability predictions. A number of objective and subjective factors are described that prevent the wide integration of mathematical methods in the practical activities of economists. It is justified [...] Read more.
This article deals with issues related to the use of mathematical methods of cash deficit probability predictions. A number of objective and subjective factors are described that prevent the wide integration of mathematical methods in the practical activities of economists. It is justified that, due to the large number of external and internal factors affecting the economic system state, the values of indicators of an economic system state are often random. The possibility of using probability theory methods to predict the occurrence of cash deficits is proved. Using empirical data including the results of thousands of observations, the possibility of using the normal distribution density function for the purpose of predicting insufficient funds for payment is illustrated. The essence of the proposed model is that it contains a prediction of a macrotrend—i.e., the risk of a cash gap—based on high-frequency microlevel data. At the same time, a prediction of the probability of a cash deficit, and not its estimation for a specific date, was made. This is the main difference between the described model and common scoring estimates. This article proposes an approach to estimate the probability of a cash deficit based on data from a specific business entity, rather than aggregated data from other organizations. Full article
(This article belongs to the Special Issue Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences)
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12 pages, 2727 KiB  
Article
Credit Risk Theoretical Model on the Base of DCC-GARCH in Time-Varying Parameters Framework
by Nikita Moiseev, Aleksander Sorokin, Natalya Zvezdina, Alexey Mikhaylov, Lyubov Khomyakova and Mir Sayed Shah Danish
Mathematics 2021, 9(19), 2423; https://doi.org/10.3390/math9192423 - 29 Sep 2021
Cited by 28 | Viewed by 2525
Abstract
The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the [...] Read more.
The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the next time period. In this paper, a new method of dealing with time-varying parameters of scoring models is proposed, which is aimed at computing the default probability of a borrower. It was empirically shown that in a continuously changing economic environment factors’ influence on a target variable is also changing. Therefore, forecasting coefficients yields a better financial result than simply applying parameters obtained by accumulated statistics over past time periods. The paper develops a new theoretical approach, incorporating a combination of the ARIMA class model, the DCC-GARCH model and the state–space model, which is more accurate, than using only the ARIMA model. Rigorous simulation testing is provided to confirm the efficiency of the proposed method. Full article
(This article belongs to the Special Issue Mathematical-Statistical Models and Qualitative Theories for Economic and Social Sciences)
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