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Statistics and Mathematics in Economics and Finance: Theory, Methods and...
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Statistics and Mathematics in Economics and Finance: Theory, Methods and Applications

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 2739

Special Issue Editors

Prof. Paolo Pagnottoni

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Guest Editor
Department of Economics and Management, University of Pavia, Via San Felice 5, 27100 Pavia, Italy
Interests: statistics; time series analysis; financial econometrics; complex systems; systemic risk; risk management; financial markets; cryptocurrencies; multivariate analysis
Prof. Domenico Scopelliti

E-Mail Website
Guest Editor
Department of Economics and Management, University of Brescia, Contrada S. Chiara 50, 25122 Brescia, Italy
Interests: mathematics for economics and finance; variational analysis; optimization; equilibrium problems
Prof. Alessandro Bitetto

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: representation learning; machine learning; mixed frequency time series analysis; synthetic financial index; graph neural networks; credit risk; clustering analysis

Special Issue Information

Dear Colleagues,

The study of economic and financial issues deals with a wide variety of subjects, including statistics, mathematics, physics, and econometrics, both from a theoretical and an applied viewpoint.  

This Special Issue is aimed at bringing together contributions from a wide variety of study fields, particularly statistics and mathematics, with applications in economics and finance. The Special Issue welcomes studies on novel statistical and mathematical methodologies and/or their novel applications in the economic and financial domains.

The Special Issue is broad, and theoretical and empirical contributions concerning any of the mentioned issues are more than welcome. Likewise, we welcome submissions of papers with socially relevant research questions. Papers in between the fields and applicative domains elicited above are more than welcome as well, and the Special Issue is open to receiving further ideas not included in the summary and keywords.

Prof. Paolo Pagnottoni
Prof. Domenico Scopelliti
Prof. Alessandro Bitetto
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

  • statistics for economics and finance
  • mathematics for economics and finance
  • econometrics
  • time series analysis
  • financial economics
  • financial mathematics
  • multivariate methods for economics and finance
  • risk management
  • machine learning methods
  • artificial intelligence in economics and finance
  • network models
  • alternative data in economics and finance
  • financial technologies
  • models of climate change impacts on economics and finance
  • optimization
  • variational inequalities
  • bilevel problems
  • equilibrium problems

Published Papers (3 papers)

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Research

27 pages, 1916 KiB  
Article
Bayesian Learning in an Affine GARCH Model with Application to Portfolio Optimization
by Marcos Escobar-Anel, Max Speck and Rudi Zagst
Mathematics 2024, 12(11), 1611; https://doi.org/10.3390/math12111611 - 21 May 2024
Viewed by 133
Abstract
This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. [...] Read more.
This paper develops a methodology to accommodate uncertainty in a GARCH model with the goal of improving portfolio decisions via Bayesian learning. Given the abundant evidence of uncertainty in estimating expected returns, we focus our analyses on the single parameter driving expected returns. After deriving an Uncertainty-Implied GARCH (UI-GARCH) model, we investigate how learning about uncertainty affects investments in a dynamic portfolio optimization problem. We consider an investor with constant relative risk aversion (CRRA) utility who wants to maximize her expected utility from terminal wealth under an Affine GARCH(1,1) model. The corresponding stock evolution, and therefore, the wealth process, is treated as a Bayesian information model that learns about the expected return with each period. We explore the one- and two-period cases, demonstrating a significant impact of uncertainty on optimal allocation and wealth-equivalent losses, particularly in the case of a small sample size or large standard errors in the parameter estimation. These analyses are conducted under well-documented parametric choices. The methodology can be adapted to other GARCH models and applications beyond portfolio optimization. Full article
(This article belongs to the Special Issue Statistics and Mathematics in Economics and Finance: Theory, Methods and Applications)
16 pages, 525 KiB  
Article
Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications
by Cinzia Di Nuzzo
Mathematics 2024, 12(4), 508; https://doi.org/10.3390/math12040508 - 6 Feb 2024
Cited by 1 | Viewed by 589
Abstract
This study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved accuracy in real-world applications. Despite achieving better clustering for datasets with [...] Read more.
This study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved accuracy in real-world applications. Despite achieving better clustering for datasets with mixed variables, challenges remain in identifying suitable kernel functions for categorical relationships. Full article
(This article belongs to the Special Issue Statistics and Mathematics in Economics and Finance: Theory, Methods and Applications)
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15 pages, 502 KiB  
Article
Three-Part Composite Pareto Modelling for Income Distribution in Malaysia
by Muhammad Hilmi Abdul Majid, Kamarulzaman Ibrahim and Nurulkamal Masseran
Mathematics 2023, 11(13), 2899; https://doi.org/10.3390/math11132899 - 28 Jun 2023
Cited by 1 | Viewed by 1222
Abstract
Income distribution models can be useful for describing the economic properties of a population. In this study, three-part composite Pareto models are fitted to the income distribution in Malaysia for the years 2007, 2009, 2012, 2014, and 2016. The three-part composite Pareto models [...] Read more.
Income distribution models can be useful for describing the economic properties of a population. In this study, three-part composite Pareto models are fitted to the income distribution in Malaysia for the years 2007, 2009, 2012, 2014, and 2016. The three-part composite Pareto models divide the population into three parts, each following a different distribution model. The lower part follows the inverse Pareto distribution, the upper part follows the Pareto distribution, and the middle part follows another unspecified distribution model. For application in income data, the use of Gaussian mixture distribution is proposed for the middle part, making the inverse Pareto–Gaussian mixture-Pareto distribution model semi-parametric. From the model, it is found that the levels of income inequality in the lower and upper income groups decrease over the period of study. Additionally, the proportion of data following the inverse Pareto distribution in the model is highly correlated with the official absolute poverty incidence. Full article
(This article belongs to the Special Issue Statistics and Mathematics in Economics and Finance: Theory, Methods and Applications)
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