Mathematical Models and Applications in Finance

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1430

Special Issue Editors

1. Associate Professor, Bond Business School, Bond University, Robina, QLD 4226, Australia
2. Honorary Senior Fellow, UQ Business School, University of Queensland, St Lucia, QLD, Australia
Interests: portfolio optimization; market risk; dependence modelling; copulas; pairs trading

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Guest Editor
Department of Business and Marketing Strategy Professor, Kindai University, Higashiosaka, Osaka 577-8502, Japan
Interests: pension funds; asset allocation; corporate finance

Special Issue Information

Dear colleagues,

In financial research, mathematical modelling allows for the establishment of a functional relationship between multiple variables, thereby being an effective method with which to analyse and solve problems in financial economics. Mathematical models underpin our understanding of many problems in financial economics, ranging from Markowitz’s (1952) mean-variance optimisation, which underpins modern portfolio theory (MPT), to the Black–Scholes–Merton model for option pricing and the Black–Litterman model, which incorporates investors’ views of expected returns in MPT. Advancements in the fields of statistical and machine learning have led towards uses in the areas of credit risk and asset pricing. In this context, we are seeking to publish high-quality research on fund management, risk management (i.e., credit, market, and operational), portfolio optimisation, asset pricing, option pricing, volatility spillovers, and any other topics related to mathematical finance. We pay particular attention to the importance of the use of big data and AI/ML techniques in financial economics. We encourage the submission of quantitative research works.

Dr. Rand Low
Prof. Dr. Yasuaki Watanabe
Guest Editors

Manuscript Submission Information

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Keywords

  • portfolio optimisation
  • asset pricing
  • credit risk
  • market risk
  • fund management
  • interest rates
  • operational risk
  • fixed income
  • commodities
  • equities
  • cryptocurrency
  • volatility modelling
  • spillovers
  • derivatives
  • factor models

Published Papers (2 papers)

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Research

9 pages, 317 KiB  
Article
Kelly Criterion Extension: Advanced Gambling Strategy
by Song-Kyoo (Amang) Kim
Mathematics 2024, 12(11), 1725; https://doi.org/10.3390/math12111725 - 1 Jun 2024
Viewed by 299
Abstract
This article introduces an innovative extension of the Kelly criterion, which has traditionally been used in gambling, sports wagering, and investment contexts. The Kelly criterion extension (KCE) refines the traditional capital growth function to better suit dynamic market conditions. The KCE improves the [...] Read more.
This article introduces an innovative extension of the Kelly criterion, which has traditionally been used in gambling, sports wagering, and investment contexts. The Kelly criterion extension (KCE) refines the traditional capital growth function to better suit dynamic market conditions. The KCE improves the traditional approach to accommodate the complexities of financial markets, particularly in stock and commodity trading. This innovative method focuses on crafting strategies based on market conditions and player actions rather than direct asset investments, which enhances its practical application by minimizing risks associated with volatile investments. This paper is structured to first outline the foundational concepts of the Kelly criterion, followed by a detailed presentation of the KCE and its advantages in practical scenarios, including a case study on its application to blackjack strategy optimization. The mathematical framework and real-world applicability of the KCE are thoroughly discussed, demonstrating its potential to bridge the gap between theoretical finance and actual trading outcomes. Full article
(This article belongs to the Special Issue Mathematical Models and Applications in Finance)
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31 pages, 12380 KiB  
Article
Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN
by Yishuai Tian and Yifan Wu
Mathematics 2024, 12(11), 1610; https://doi.org/10.3390/math12111610 - 21 May 2024
Viewed by 566
Abstract
Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to [...] Read more.
Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures. Full article
(This article belongs to the Special Issue Mathematical Models and Applications in Finance)
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