Advancements in Applied Mathematics for Economic Data Analytics: Models, Methods, and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 643

Special Issue Editors


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Guest Editor
Department of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, Italy
Interests: risk analysis; wavelet analysis; risk management; time-series predictability; cyber insurance; investment in cyber security
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Guest Editor
Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Italy
Interests: time series analysis; spatial statistics; clustering; forecasting; financial econometrics
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Guest Editor
Department of Economics and Business, University of Almería, 04120 Almería, Spain
Interests: long memory; portfolio theory; fractal dimension; financial markets; econophysics
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Special Issue Information

Dear Colleague,

In the field of applied mathematics, the utilization of mathematical models holds significant promise in unraveling the intricacies of economic phenomena. This introductory Special Issue will take readers on a journey through the nexus of mathematics and economics, where mathematical frameworks serve as potent tools for understanding, analyzing, and forecasting various facets of economic activity. By leveraging mathematical methodologies, researchers and practitioners alike will endeavor to glean actionable insights, inform decision-making processes, and contribute to the advancement of economic theory and practice. Keywords: mathematical models; mathematical models applied to economics and finance; time-series analysis; forecasting

The Special Issue is focused on (but not limited to) these fields:

Economic Forecasting Models: Exploring mathematical methods for predicting economic indicators. Financial Data Analysis: Delving into mathematical techniques for analyzing financial markets, including asset-pricing models, portfolio optimization, risk management, etc. Computational Econometrics: Examining the use of computational methods for economic data analysis, including machine learning algorithms, big data analysis, etc. Economic Modeling: Discussing mathematical models for complex economic phenomena such as business cycles, consumer behavior, investment decisions, etc. Practical Applications: Exploring case studies and real-world applications of the mathematical analysis of economic data in sectors such as finance, business economics, economic policy, etc.

Dr. Alessandro Mazzoccoli
Dr. Raffaele Mattera
Prof. Dr. J.E. Trinidad-Segovia
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

  • mathematical models
  • applied mathematics
  • mathematical models for economics
  • mathematical models for finance
  • data analysis
  • forecasting
  • differential equations
  • stochastic differential equations
  • dynamical systems

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Published Papers (1 paper)

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Research

19 pages, 1414 KiB  
Article
Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty
by Lyubov Doroshenko, Loretta Mastroeni and Alessandro Mazzoccoli
Mathematics 2025, 13(8), 1346; https://doi.org/10.3390/math13081346 - 20 Apr 2025
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Abstract
The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate [...] Read more.
The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate the relationship between these indices and commodity prices across multiple time scales. The wavelet approach captures complex, time-varying dependencies, offering a more nuanced understanding of how uncertainty indices influence commodity price fluctuations. By integrating this analysis with predictability measures, we assess how uncertainty indices enhance forecasting accuracy. We further incorporate deep learning models capable of capturing sequential patterns in financial time series into our analysis to better evaluate their predictive potential. Our findings highlight the varying impact of financial and economic uncertainty on the predictability of commodity prices, showing that while some indices offer valuable forecasting information, others display strong correlations without significant predictive power. These results underscore the need for tailored predictive models, as different commodities react differently to the same financial conditions. By combining wavelet-based measures with machine learning techniques, this study presents a comprehensive framework for evaluating the role of uncertainty in commodity markets. The insights gained can support investors, policymakers, and market analysts in making more informed decisions. Full article
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