The New Advances in Mathematical Economics and Financial Modelling

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1629

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Instituto Federal de Educação Ciência e Tecnologia da Paraíba—IFPB, Joao Pessoa, Brazil
Interests: probability; mathematical statistics; statistical physics; applied probability; applied statistics; applied mathematics; information theory; multifractal analysis; data analysis; time series analysis

Special Issue Information

Dear Colleagues,

The use of mathematics in the professional environment has an impact on the economy, as many professions require the ability to establish standards, correlations, error margins, and accuracy rates. The benefits are noticeable in different areas and are also linked to greater income generation.

Mathematical economics consists of the application of mathematics to the development of economic models with the purpose of building a rigorous and unified economic theory. For example, the techniques of functional analysis, topology, and differential topology are widely used in the central economic model.

In this Special Issue, we are also open to articles on statistical physics and econophysics, through which it is possible to increase company profits, increase the quality of processes or products, minimize costs, make decisions of political or economic value, and increase critical analysis, among others.

Prof. Dr. Fernando Henrique Antunes De Araújo
Guest Editor

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Keywords

  • mathematical modelling
  • statistical physics
  • econophysics
  • economics and finance

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Published Papers (2 papers)

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Research

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32 pages, 3828 KiB  
Article
Volatility Spillovers Among EAGLE Economies: Insights from Frequency-Based TVP-VAR Connectedness
by Yakup Ari, Hakan Kurt and Harun Uçak
Mathematics 2025, 13(8), 1256; https://doi.org/10.3390/math13081256 - 11 Apr 2025
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Abstract
This study aims to reveal the network connectedness between the volatilities of Emerging and Growth-Leading Economies (EAGLEs) stock exchanges with the frequency-based TVP-VAR connectedness approach. Connectedness results were obtained in short (1–5 days) and long (5-inf) period frequencies among the volatilities obtained with [...] Read more.
This study aims to reveal the network connectedness between the volatilities of Emerging and Growth-Leading Economies (EAGLEs) stock exchanges with the frequency-based TVP-VAR connectedness approach. Connectedness results were obtained in short (1–5 days) and long (5-inf) period frequencies among the volatilities obtained with the Garman–Klass volatility estimator. According to the dynamic TCI results, connectivity peaked during the COVID-19 and Russia–Ukraine War periods. BVSP is the most dominant transmitter of the network and spreads the most effect to the emerging markets. As a result of the pairwise metrics, SSE has the lowest values and is positioned as a relatively independent market in the network. In particular, SSE has almost no connection with BIST in the short term, while it has a more significant effect on BIST in the long term. Moreover, the connectedness metrics show that MOEX is in a neutral position in the network and is largely affected by its internal dynamics. Full article
(This article belongs to the Special Issue The New Advances in Mathematical Economics and Financial Modelling)
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Review

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26 pages, 6925 KiB  
Review
Sectoral Efficiency and Resilience: A Multifaceted Analysis of S&P Global BMI Indices Under Global Crises
by Milena Kojić, Slobodan Rakić, José Wesley Lima da Silva and Fernando Henrique Antunes de Araujo
Mathematics 2025, 13(4), 641; https://doi.org/10.3390/math13040641 - 15 Feb 2025
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Abstract
This study investigates the complexity, efficiency, and sectoral interdependencies of the S&P Global BMI indices during critical global events, including the COVID-19 pandemic and the Russia–Ukraine war. The analysis is conducted in three dimensions: (1) evaluating market efficiency using permutation entropy and the [...] Read more.
This study investigates the complexity, efficiency, and sectoral interdependencies of the S&P Global BMI indices during critical global events, including the COVID-19 pandemic and the Russia–Ukraine war. The analysis is conducted in three dimensions: (1) evaluating market efficiency using permutation entropy and the Fisher information measure, (2) exploring sectoral alignments through clustering techniques (hierarchical and k-means clustering), and (3) assessing the influence of geopolitical risk using Multifractal Detrended Cross-Correlation Analysis (MFDCCA). The results highlight significant variations in informational efficiency across sectors, with Utilities and Consumer Staples exhibiting high efficiency, while Emerging Markets and Financials reflect lower efficiency levels. Temporal analysis reveals widespread efficiency declines during the pandemic, followed by mixed recovery patterns during the Ukraine conflict. Clustering analysis uncovers dynamic shifts in sectoral relationships, emphasizing the resilience of defensive sectors and the unique behavior of Developed BMI throughout crises. MFDCCA further demonstrates the multifractality in cross-correlations with geopolitical risk, with Consumer Staples and Energy showing stable persistence and Information Technology exhibiting sensitive complexity. These findings emphasize the adaptive nature of global markets in response to systemic and geopolitical shocks, offering insights for risk management and investment strategies. Full article
(This article belongs to the Special Issue The New Advances in Mathematical Economics and Financial Modelling)
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