Special Issue "Mathematical and Statistical Methods Applications in Finance"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Dr. Miltiadis Chalikias
E-Mail Website
Guest Editor
Department of Accounting and Finance, University of West Attica, GR12244 Egaleo, Greece
Interests: applied statistics; applied mathematics; experimental designs; operations research
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Over recent decades, mathematical and statistical models’ applications in finance have become more and more popular both in a managerial and research context. On the one hand, there are specific aspects of mathematics with a high impact on finance, such as the models dealing with portfolio theory, capital pricing and risk management. On the other hand, statistical models have many applications in finance as well, such as linear and non-linear time series or statistical tools.  The aim of this Special Issue is to bridge the gap between the classical models of mathematics and statistics and their applications in real cases of finance. Authors can submit their contributions concerning applications or developments of mathematical and statistical models in finance topics such as risky assets, discrete time market models, portfolio management, forward and future contracts, option pricing, stochastic interest rates, portfolio theory, capital pricing, risk-neural probabilities, financial multiperiod markets, ARMA process, ARIMA processes, seasonal models, maximum likelihood estimation, quasi-maximum likelihood, Kernel estimators in time series, dynamical systems, stochastic integrals, stochastics differential equations, classical arbitrage theory, Fourier methods, stochastic interest rate models, and discrete time approximation.  

It is my pleasure to invite authors to contribute to this Special Issue by submitting research articles that will be subject to rigorous peer review aiming to contribute to the development of the relevant research field.

Dr. Miltiadis Chalikias
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 papers will be 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 1600 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
  • Statistical models
  • Finance
  • Financial mathematics
  • Portfolio theory
  • Risk management
  • Linear time series
  • Non-linear time series

Published Papers (8 papers)

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Research

Article
Profiting on the Stock Market in Pandemic Times: Study of COVID-19 Effects on CESEE Stock Markets
Mathematics 2021, 9(17), 2077; https://doi.org/10.3390/math9172077 (registering DOI) - 27 Aug 2021
Viewed by 169
Abstract
This research deals with stock market reactions of Central Eastern and South Eastern European (CESEE) markets to the COVID-19 pandemic, via the event study methodology approach. Since the stock markets react quickly to certain announcements, the used methodology is appropriate to evaluate how [...] Read more.
This research deals with stock market reactions of Central Eastern and South Eastern European (CESEE) markets to the COVID-19 pandemic, via the event study methodology approach. Since the stock markets react quickly to certain announcements, the used methodology is appropriate to evaluate how the aforementioned markets reacted to certain events. The purpose of this research was to evaluate possibilities of obtaining profits on the stock markets during great turbulences, when a majority of the participants panic. More specifically, the contrarian trading strategies are observed if they can obtain gains, although a majority of the markets suffer great losses during pandemic shocks. The contributions to the existing literature of this research are as follows. Firstly, empirical research on CESEE stock markets regarding other relevant topics is still scarce and should be explored more. Secondly, the event study approach of COVID-19 effects utilized in this study has (to the knowledge of the author) not yet been explored on the aforementioned markets. Thirdly, based on the results of CESEE market reactions to specific announcements regarding COVID-19, a simulation of simple trading strategies will be made in order to estimate whether some investors could have profited in certain periods. The results of the study indicate promising results in terms of exploiting other investors’ panicking during the greatest decline of stock market indices. Namely, the initial results, as expected, indicate strong negative effects of specific COVID-19 announcements on the selected stock markets. Secondly, the obtained information was shown to be useful for contrarian strategy in order to exploit great dips in the stock market indices values. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Covariance Principle for Capital Allocation: A Time-Varying Approach
Mathematics 2021, 9(16), 2005; https://doi.org/10.3390/math9162005 - 21 Aug 2021
Viewed by 390
Abstract
The covariance allocation principle is one of the most widely used capital allocation principles in practice. Risks change over time, so capital risk allocations should be time-dependent. In this paper, we propose a dynamic covariance capital allocation principle based on the variance-covariance of [...] Read more.
The covariance allocation principle is one of the most widely used capital allocation principles in practice. Risks change over time, so capital risk allocations should be time-dependent. In this paper, we propose a dynamic covariance capital allocation principle based on the variance-covariance of risks that change over time. The conditional correlation of risks is modeled by means of a dynamic conditional correlation (DCC) model. Unlike the static approach, we show that in our dynamic capital allocation setting, the distribution of risk capital allocations can be estimated, and the expected future allocations of capital can be predicted, providing a deeper understanding of the stochastic multivariate behavior of risks. The methodology presented in the paper is illustrated with an example involving the investment risk in a stock portfolio. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Trading Cryptocurrencies as a Pandemic Pastime: COVID-19 Lockdowns and Bitcoin Volume
Mathematics 2021, 9(15), 1771; https://doi.org/10.3390/math9151771 - 27 Jul 2021
Viewed by 423
Abstract
This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low [...] Read more.
This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Teaching CAPM for a Pre-Finance Graduate Program at the STEM Undergraduate Level: Linear Algebra Perspective
Mathematics 2021, 9(14), 1668; https://doi.org/10.3390/math9141668 - 15 Jul 2021
Viewed by 313
Abstract
Students considering a masters in Finance Engineering or Artificial Intelligence in Finance are usually required to have an undergraduate background in science, technology, engineering, or mathematics (STEM). STEM students have a good capacity in mathematics and science, but they may not have studied [...] Read more.
Students considering a masters in Finance Engineering or Artificial Intelligence in Finance are usually required to have an undergraduate background in science, technology, engineering, or mathematics (STEM). STEM students have a good capacity in mathematics and science, but they may not have studied financial theory. To facilitate the classroom teaching of the Capital Asset Pricing Model (CAPM) for STEM students, this paper seeks to expound on the essence of the theory starting at a two-asset framework. Adopting the concepts proposed by Merton (1972), this paper accomplishes the derivation by virtue of basic mathematical tools such as linear algebra, geometry, and statistics except for calculus. We show that the major aspects of Merton’s derivation of the CAPM for a universe of N assets may also be obtained in a two-asset world. Through the methods of this article, students will learn the in-depth theory of CAPM and its hands-on empirical tool. For example, students will realize that even if investors specify different threshold rewards, their different CAPMs will yield identical pricing for assets and portfolios. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Alternative Financial Methods for Improving the Investment in Renewable Energy Companies
Mathematics 2021, 9(9), 1047; https://doi.org/10.3390/math9091047 - 06 May 2021
Viewed by 413
Abstract
Renewable energies have increased in importance in recent years due to the harm caused to the environment by fossil fuels. As a result, renewable energy companies seem to be profitable investment opportunities given their likely substantial future earnings. However, previous empirical evidence has [...] Read more.
Renewable energies have increased in importance in recent years due to the harm caused to the environment by fossil fuels. As a result, renewable energy companies seem to be profitable investment opportunities given their likely substantial future earnings. However, previous empirical evidence has not always agreed about this likely profitability. In addition, the methodologies employed in the existing empirical literature are complicated and not feasible for most investors to use. Therefore, it is proposed an approach which combines the use of performance measures, screening rules, devolatized returns and portfolio strategies, all of which can be implemented by investors. This approach results in high cumulative returns of more than 200% and other positive ratios, even when transaction costs are considered. This should encourage people to invest in these renewable energies and contribute to improving the environment. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Spillover and Drivers of Uncertainty among Oil and Commodity Markets
Mathematics 2021, 9(4), 441; https://doi.org/10.3390/math9040441 - 23 Feb 2021
Cited by 2 | Viewed by 581
Abstract
The paper aims to examine the spillover of uncertainty among commodity markets using Diebold–Yilmaz approach based on forecast error variance decomposition. Next, causal impact of global factors as drivers of uncertainty transmission between oil and other commodity markets is analyzed. Our analysis suggests [...] Read more.
The paper aims to examine the spillover of uncertainty among commodity markets using Diebold–Yilmaz approach based on forecast error variance decomposition. Next, causal impact of global factors as drivers of uncertainty transmission between oil and other commodity markets is analyzed. Our analysis suggests that oil is a net transmitter to other commodity uncertainties, and this transmission significantly increased during the global financial crisis of 2008–2009. The use of linear and nonlinear causality tests indicates that the global factors have a causal effect on the overall connectedness, and especially on the spillovers from oil to other commodity uncertainties. Further segregation of transmissions between oil to individual commodity markets indicates that stock market implied volatility, risk spread, and economic policy uncertainty are the influential drivers of connectedness among commodity markets. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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Article
Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector
Mathematics 2021, 9(4), 297; https://doi.org/10.3390/math9040297 - 03 Feb 2021
Viewed by 462
Abstract
A part of the financial literature has attempted to explain idiosyncratic asset shocks through investor behavior in response to company news and events. As a result, there has been an increase in the development of different investor sentiment measurements. This paper analyses whether [...] Read more.
A part of the financial literature has attempted to explain idiosyncratic asset shocks through investor behavior in response to company news and events. As a result, there has been an increase in the development of different investor sentiment measurements. This paper analyses whether the Bloomberg investor sentiment index has a causal relationship with the abnormal returns and volume shocks of major European Union (EU) financial companies through a sample of 85 financial institutions over 4 years (2014–2018) on a daily basis. The i.i.d. shocks are obtained from a factorial asset pricing model and ARMA-GARCH-type process; then we checked whether there is both individual and joint causality between the standardized residuals. The results show that the explanatory capacity of the shocks of the firm Bloomberg sentiment index is low, although there is empirical evidence that the effects correspond more to the situation of the financial subsector (banks, real estate, financial services and insurance) than to the company itself, with which we conclude that the sentiment index analyzed reflects a sectorial effect more than individual one. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
Article
The Lie Algebraic Approach for Determining Pricing for Trade Account Options
Mathematics 2021, 9(3), 279; https://doi.org/10.3390/math9030279 - 30 Jan 2021
Viewed by 623
Abstract
In recent years, many advanced techniques have been applied to financial problems; however, very few scholars have used the Lie theory. The purpose of this study was to examine the options for a trade account through Lie symmetry analysis. According to our results, [...] Read more.
In recent years, many advanced techniques have been applied to financial problems; however, very few scholars have used the Lie theory. The purpose of this study was to examine the options for a trade account through Lie symmetry analysis. According to our results, it is effective for determining analytical solutions for pricing issues and solving other partial differential equations. The proposed solution can be used by further researchers or practitioners in option pricing problems for better performance compared with the classical Black–Scholes model. Full article
(This article belongs to the Special Issue Mathematical and Statistical Methods Applications in Finance)
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