Mathematical Finance: Statistical Inference, Stochastic Modeling, and Advanced Algorithms

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1817

Special Issue Editor


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Guest Editor
Department of Economics, School of Economics, Business Administration and Accounting at Ribeirão Preto (FEA-RP/USP), University of São Paulo, Av. dos Bandeirantes 3900, Ribeirão Preto 14040-905, SP, Brazil
Interests: econometrics; statistical and computational methods in finance

Special Issue Information

Dear Colleagues,

We are pleased to extend an invitation to scholars specializing in Mathematical Finance, Financial Econometrics, and Statistics to contribute to our forthcoming Special Issue titled 'Mathematical Finance: Statistical Inference, Stochastic Modeling, and Advanced Algorithms'. We welcome submissions of research papers that introduce innovative methods, algorithms, and practical applications in the realm of statistical inference as applied to the modeling of asset prices.

This Special Issue aims to encompass a broad spectrum of topics, including, but not limited to:

  • Modeling of financial time series;
  • Derivatives pricing;
  • Term structure of interest rates;
  • Stochastic volatility and risk modeling;
  • Price duration analysis;
  • High-frequency data analysis.

We are particularly interested in approaches utilizing both frequentist and Bayesian inference methodologies. This encompasses techniques such as:

  • Sequential Monte Carlo and Particle Filtering;
  • Markov Chain Monte Carlo;
  • Laplace Approximations;
  • Machine Learning methods;
  • Other approximating techniques, including Random Forests, Deep Learning, Reinforcement Learning, and Non-parametric estimators.

Dr. Márcio Poletti Laurini
Guest Editor

Manuscript Submission Information

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Keywords

  • modeling of financial time series
  • derivatives pricing
  • term structure of interest rates
  • stochastic volatility and risk modeling
  • price duration analysis
  • high-frequency data analysis
  • sequential Monte Carlo and particle filtering
  • Markov chain Monte Carlo
  • laplace approximations
  • machine learning methods
  • other approximating techniques, including random forests, deep Learning, reinforcement learning, and non-parametric estimators

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Published Papers (2 papers)

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Research

13 pages, 1059 KiB  
Article
Time Series Analysis of the Dynamics of Merger and Acquisition Cycles in the Global Water Sector
by Manuel Monge, Rafael Hurtado and Juan Infante
Mathematics 2025, 13(7), 1146; https://doi.org/10.3390/math13071146 - 31 Mar 2025
Viewed by 201
Abstract
This paper examined the cyclical patterns of mergers and acquisitions (M&A) in the global water sector from 1982 to 2024, with a focus on both linear and nonlinear dynamics in M&A waves. Through a univariate analysis using ARFIMA models, we found that the [...] Read more.
This paper examined the cyclical patterns of mergers and acquisitions (M&A) in the global water sector from 1982 to 2024, with a focus on both linear and nonlinear dynamics in M&A waves. Through a univariate analysis using ARFIMA models, we found that the data exhibited stationary behavior, meaning that in response to an exogenous shock, the series is likely to revert to its original trend over time. Additionally, the non-parametric Brock, Dechert, and Scheinkman (BDS) test revealed the complex and irregular nature of M&A cycles within the sector. To account for this complexity, we applied the Markov-switching dynamic regression (MS-DR) model, which shows that once the industry enters a high-activity regime, it tends to persist in this state for extended periods. This suggests that external shocks or trends—such as regulatory reforms or global water scarcity concerns—are key drivers that trigger and sustain waves of M&A activity in the sector. Full article
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29 pages, 695 KiB  
Article
On the Calibration of the Kennedy Model
by Dalma Tóth-Lakits and Miklós Arató
Mathematics 2024, 12(19), 3059; https://doi.org/10.3390/math12193059 - 29 Sep 2024
Viewed by 1193
Abstract
The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon–Nikodym derivatives for [...] Read more.
The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon–Nikodym derivatives for enhanced accuracy. We introduce an efficient simulation method for the Kennedy field and develop a Black–Scholes-like analytical pricing formula for diverse financial assets. Additionally, we present a novel parameter estimation algorithm grounded in numerical extreme value optimization, enabling the recalibration of parameters based on observed financial product prices. To validate the efficacy of our approach, we assess its performance using real-world par swap rates in the latter part of this article. Full article
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