Advances in Financial Modeling

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

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 5431

Special Issue Editors


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Guest Editor
Department of Accounting and Finance, Hellenic Mediterranean University, Heraklion, Greece
Interests: financial economics; financial econometrics; risk management; banking
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 83200 Samos, Greece
Interests: functional analysis (partially ordered linear spaces); convex analysis; vector optimization; financial mathematics (mathematical aspects of risk measurement and rsk management; derivatives’ pricing); mathematical economics (general equilibrium theory)
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Accounting and Finance, Hellenic Mediterranean University, Heraklion, Greece
Interests: computational statistics; digital finance; extreme value theory; financial econometrics; quantitative finance; risk management; volatility and times series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The importance of financial modeling is increasing rapidly. Mathematical models are designed to represent the performance of any financial asset, while computational and quantitative methods have become important tools for financial decision making. Since the work of the French Mathematician Louis Bachelier (published in 1900) on the Brownian model and its use for valuing stock options, many sophisticated models have become available which can be used by financial economists and risk managers. Additionally, new mathematical techniques are continuously developed and used to solve financial problems including risk analysis, asset pricing and portfolio management.

This Special Issue covers the rapidly growing field of financial modeling and attempts to explore and bring together theoretical, practical and state-of-the-art applications in modern financial problems. Authors are invited to submit high-quality papers which discuss original, unpublished research in related scientific areas. All contributions should bridge the gap between theory and practice in financial modeling and will be of interest to both researchers and practitioners. The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in different fields of portfolio selection and management, pricing derivatives, volatility modeling, risk analysis, stochastic modeling and asset pricing, among others.

Prof. Dr. Christos Floros
Dr. Christos Kountzakis
Dr. Konstantinos Gkillas
Guest Editors

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. 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 2600 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

  • financial economics
  • financial econometrics
  • financial risk management
  • financial engineering
  • mathematical finance
  • quantitative finance
  • applied statistics and operational research in finance

Published Papers (4 papers)

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Research

18 pages, 369 KiB  
Article
A Stochastic Weather Model for Drought Derivatives in Arid Regions: A Case Study in Qatar
by Jayeong Paek, Marco Pollanen and Kenzu Abdella
Mathematics 2023, 11(7), 1628; https://doi.org/10.3390/math11071628 - 28 Mar 2023
Cited by 3 | Viewed by 957
Abstract
In this paper, we propose a stochastic weather model consisting of temperature, humidity, and precipitation, which is used to calculate a reconnaissance drought index (RDI) in Qatar. The temperature and humidity models include stochastic differential equations and utilize an adjusted Ornstein–Uhlenbeck [...] Read more.
In this paper, we propose a stochastic weather model consisting of temperature, humidity, and precipitation, which is used to calculate a reconnaissance drought index (RDI) in Qatar. The temperature and humidity models include stochastic differential equations and utilize an adjusted Ornstein–Uhlenbeck (O–U) process. For the precipitation model, a first-order Markov chain is used to differentiate between wet and dry days and the precipitation amount on wet days is determined by a probability distribution. Five different probability distributions were statistically tested to obtain an appropriate precipitation amount. The evapotranspiration used in the RDI calculation incorporates crop coefficient values, depends on the growth stages of the crops, and provides a crop-specific and more realistic representation of the drought conditions. Five different evapotranspiration formulations were investigated in order to obtain the most accurate RDI values. The calculated RDI was used to assess the intensity of drought in Doha, Qatar, and could be used for the pricing of financial drought derivatives, a form of weather derivative. These derivatives could be used by agricultural producers to hedge against the economic effects of droughts. Full article
(This article belongs to the Special Issue Advances in Financial Modeling)
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16 pages, 363 KiB  
Article
“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis
by Nicolas S. Magner, Nicolás Hardy, Tiago Ferreira and Jaime F. Lavin
Mathematics 2023, 11(7), 1591; https://doi.org/10.3390/math11071591 - 25 Mar 2023
Viewed by 1287
Abstract
This paper studies the predictability of implied volatility indices of stocks using financial reports tone disagreement from U.S. firms. For this purpose, we build a novel measure of tone disagreement based on financial report tone synchronization of U.S. corporations scattered across five Fama-French [...] Read more.
This paper studies the predictability of implied volatility indices of stocks using financial reports tone disagreement from U.S. firms. For this purpose, we build a novel measure of tone disagreement based on financial report tone synchronization of U.S. corporations scattered across five Fama-French industries. The research uses tree network methods to calculate the minimum spanning tree length utilizing data from text mining sentiments features extracted from all U.S. firms that considers 837,342 financial reports. The results show that periods of increased disagreement predict higher implied volatility indices. We contribute to the literature that proposes that a high level of expectations dispersion leads to higher stock volatility and fills a gap in understanding how firms’ disagreement level of financial report tone forecast the aggregate stock market behavior. The findings also have implications for financial stability and delegated portfolio management, as accurate volatility prediction is critical for practitioners. Full article
(This article belongs to the Special Issue Advances in Financial Modeling)
24 pages, 947 KiB  
Article
Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models
by Shay Kee Tan, Kok Haur Ng and Jennifer So-Kuen Chan
Mathematics 2023, 11(1), 13; https://doi.org/10.3390/math11010013 - 20 Dec 2022
Viewed by 1298
Abstract
This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson [...] Read more.
This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain the fitted volatilities. Then covariances are calculated to construct the fitted variance–covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba–Engle–Kraft–Kroner generalised autoregressive conditional heteroskedasticity models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using backtests to confirm the accuracy of the VaR forecasts. Full article
(This article belongs to the Special Issue Advances in Financial Modeling)
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12 pages, 977 KiB  
Article
Generalized Johnson Distributions and Risk Functionals
by Christos Floros, Konstantinos Gkillas and Christos Kountzakis
Mathematics 2022, 10(17), 3200; https://doi.org/10.3390/math10173200 - 05 Sep 2022
Viewed by 1138
Abstract
In this paper, we study the generalized Johnson distributions’ class and its applications in finance and risk theory. The recent literature on Johnson distributions displays a better gooodness of fitting for data coming from financial markets, such as portfolio returns. However, a gereral [...] Read more.
In this paper, we study the generalized Johnson distributions’ class and its applications in finance and risk theory. The recent literature on Johnson distributions displays a better gooodness of fitting for data coming from financial markets, such as portfolio returns. However, a gereral question in risk theory and finance is the following: Which class of distributions is more appropriate in order to determine the behaviour of data coming from financial markets and insurance claims? Another question is the following one: Is ther any class of distributions that is appropriate for calculations related to any kind of risk faced by financial isntitutions and insurance companies? The answer proposed to these questions is the use of generalized Johnson’s distributions. The parameters of such distributions are estimated by the order statistics of a single or more samples. Risk functionals represent a unified approach comprising every kind of risk metric. Risk functionals include value-at-risk and expected shortfall, coherent risk measures, and endpoints and thresholds. We deduce that the risk functionals sastisfy convexity—like properties with respect to finitely-mixed distributions. We also prove in detail that the empirical distribution is a reasonable way for the estimation of the above risk functionals. In the Appendix, we provide two numerical examples for fitting samples of portfolio returns under the Johnson’s transformation. Full article
(This article belongs to the Special Issue Advances in Financial Modeling)
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