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Axioms
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Mathematical Analysis for Financial Modelling
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Mathematical Analysis for Financial Modelling

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 7026

Special Issue Editor

Prof. Dr. Ji-Hun Yoon

E-Mail Website
Guest Editor
Department of Mathematics, Pusan National University, Busan 46241, Korea
Interests: financial mathematics; stochastic differential equation

Special Issue Information

Dear Colleagues,

In this Special Issue, entitled “Mathematical Analysis for Financial Modelling”, we deal with research closely related to mathematical modelling in finance. We focus on novel and innovative methods to derive solutions from financial mathematical modelling for the interpretation of financial perspectives in financial markets. We want many experts to submit the research papers on specific topics, such as option pricing, portfolio optimization, artificial neural networks in finance, stock market forecasting, algorithm trading, and statistical methods for data fitting in finance.

Prof. Dr. Ji-Hun Yoon
Guest Editor

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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • Option Pricing
  • Portfolio Optimization
  • Algorithm trading
  • Stock market forecasting
  • Statistical techniques for the data fitting
  • The simulation of financial data
  • Machine Learning in Finance

Published Papers (3 papers)

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Research

17 pages, 883 KiB  
Article
An Application of the Super-SBM MAX and LTS(A,A,A) Models to Analyze the Business Performance of Hydropower Suppliers in Vietnam
by Thi Kim Lien Nguyen, Huu Ngoan Nguyen, Van Dan Dinh, Quoc Ngu Nguyen and Van Hung Le
Axioms 2022, 11(5), 238; https://doi.org/10.3390/axioms11050238 - 20 May 2022
Cited by 1 | Viewed by 1837
Abstract
As Vietnam continues to industrialize and modernize, such economic development and high-tech will require a major electrical energy source to operate the electrical equipment; hence, the hydropower plants are established and growing up to demand. Therefore, the purpose of this study is to [...] Read more.
As Vietnam continues to industrialize and modernize, such economic development and high-tech will require a major electrical energy source to operate the electrical equipment; hence, the hydropower plants are established and growing up to demand. Therefore, the purpose of this study is to evaluate the business performance of Vietnamese hydropower suppliers by integrating the LTS(A,A,A) model of the Additive Holt-winters method in Tableau and a super-slacks-based measure (super-SBM) max model in data envelopment analysis (DEA). The LTS(A,A,A) model is applied to forecast future valuation from 2022 to 2025 based on historical time series from 2012 to 2021. Next, with the actual and predicted data, the researcher uses the super-SBM max model to calculate the business performance of these hydropower suppliers from past to future. The empirical result reveals efficient and inefficient cases to explore which hydropower suppliers can achieve the business performance in their operational process. The position of hydropower suppliers in Vietnam from past to future time is determined particularly based on their scores every year. Further, the empirical result recommends a solution to deal with inefficient cases by deducting the input excesses and raising the output shortages based on the principle of the super-SBM Max model in DEA. The finding results create an overview of the operational process with the continuing variations in each period to equip hydropower suppliers in Vietnam which will determine their future and operational orientation. Full article
(This article belongs to the Special Issue Mathematical Analysis for Financial Modelling)
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21 pages, 6473 KiB  
Article
Analysis of the Term Structure of Major Currencies Using Principal Component Analysis and Autoencoders
by Soo Chang Chae and Sun-Yong Choi
Axioms 2022, 11(3), 135; https://doi.org/10.3390/axioms11030135 - 15 Mar 2022
Cited by 3 | Viewed by 2584
Abstract
Recently, machine-learning algorithms and existing financial data-analysis methods have been actively studied. Although the term structure of government bonds has been well-researched, the majority of studies only analyze the characteristics of one country in detail using one method. In this paper, we analyze [...] Read more.
Recently, machine-learning algorithms and existing financial data-analysis methods have been actively studied. Although the term structure of government bonds has been well-researched, the majority of studies only analyze the characteristics of one country in detail using one method. In this paper, we analyze the term structure and determine the common factors using principal component analysis (PCA) and an autoencoder (AE). We collected data on the government bonds of three countries with major currencies (the US, the UK, and Japan), extracted features, and compared them. In the PCA-based analysis, we reduced the number of dimensions by converting the normalized data into a covariance matrix and checked the first five principal components visually using graphs. In the AE-based analysis, the model consisted of two encoder layers, one middle layer, and two decoder layers, and the number of nodes in the middle layer was adjusted from one to five. As a result, no significant similarity was found for each country in the dataset, and it was appropriate to extract three features in both methods. Each feature extracted by PCA and the AE had a completely different form, and this appears to be due to the differences in the feature extraction methods. In the case of PCA, the volatility of the datasets affected the features, but in the case of AE, the results seemed to be more affected by the size of the dataset. Based on the findings of this study, this topic can be expanded to compare the results of other machine-learning algorithms or countries. Full article
(This article belongs to the Special Issue Mathematical Analysis for Financial Modelling)
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19 pages, 452 KiB  
Article
Optimal Consumption, Investment, and Housing Choice: A Dynamic Programming Approach
by Qi Li and Seryoong Ahn
Axioms 2022, 11(3), 127; https://doi.org/10.3390/axioms11030127 - 11 Mar 2022
Cited by 3 | Viewed by 2086
Abstract
We investigate a portfolio selection problem involving an agent’s realistic housing service choice, where the agent not only has to choose the size of house to live in, but also has to select between renting and purchasing a house. Adopting a dynamic programming [...] Read more.
We investigate a portfolio selection problem involving an agent’s realistic housing service choice, where the agent not only has to choose the size of house to live in, but also has to select between renting and purchasing a house. Adopting a dynamic programming approach, we derive a closed-form solution to obtain the optimal policies for the consumption, investment, housing service, and purchasing time for a house. We also present various numerical demonstrations showing the impacts of parameters in the financial and housing markets and the agent’s preference, which visually show the economic implications of our model. Our model makes a significant contribution because it is a pioneering model for the optimal time to purchase a house, which has not been investigated in depth in existing mathematical portfolio optimization models. Full article
(This article belongs to the Special Issue Mathematical Analysis for Financial Modelling)
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