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Mathematics
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Financial Modeling
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Financial Modeling

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 15246

Special Issue Editors

Prof. Dr. Christos Floros

E-Mail Website
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
Dr. Christos Kountzakis

E-Mail Website
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
Dr. Konstantinos Gkillas

E-Mail Website
Guest Editor
Department of Business Administration, University of Patras, University Campus, 26504 Rio Achaia, 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 that can be used by financial economists and risk managers. Additionally, new mathematical techniques are being 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; it is an attempt 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 describing 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, asset pricing, and 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 (5 papers)

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Research

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14 pages, 7408 KiB  
Article
Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT
by Bikramaditya Ghosh, Spyros Papathanasiou, Nikita Ramchandani and Dimitrios Kenourgios
Mathematics 2021, 9(9), 1003; https://doi.org/10.3390/math9091003 - 28 Apr 2021
Cited by 10 | Viewed by 2651
Abstract
We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone [...] Read more.
We herein employ an alternative approach to model the financial bubbles prior to crashes and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal, and Spain) during Brexit. These countries represent the five financially troubled economies of the Eurozone that have suffered the most during the Brexit referendum. It was found that all 77 crashes across the five IIGPS nations from 19 January 2015 until 17 February 2020 strictly followed a log-periodic power law or other LPPL signature. They all had a speculative bubble phase (following the power law growth) that was then followed by a sudden crash immediately after reaching a critical point. Furthermore, their pattern coefficients were similar as well. This study would surely assist policymakers around the Eurozone to predict future crashes with the help of these parameters. Full article
(This article belongs to the Special Issue Financial Modeling)
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11 pages, 298 KiB  
Article
The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling
by Olga Fullana, Mariano González and David Toscano
Mathematics 2021, 9(5), 513; https://doi.org/10.3390/math9050513 - 02 Mar 2021
Cited by 3 | Viewed by 4601
Abstract
In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling [...] Read more.
In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends. Full article
(This article belongs to the Special Issue Financial Modeling)
15 pages, 846 KiB  
Article
Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique
by Fazlollah Soleymani, Houman Masnavi and Stanford Shateyi
Mathematics 2021, 9(1), 17; https://doi.org/10.3390/math9010017 - 23 Dec 2020
Cited by 7 | Viewed by 2743
Abstract
Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this [...] Read more.
Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time. Full article
(This article belongs to the Special Issue Financial Modeling)
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11 pages, 860 KiB  
Article
Risk Appetite and Jumps in Realized Correlation
by Riza Demirer, Konstantinos Gkillas, Christos Kountzakis and Amaryllis Mavragani
Mathematics 2020, 8(12), 2255; https://doi.org/10.3390/math8122255 - 21 Dec 2020
Cited by 2 | Viewed by 1662
Abstract
This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power [...] Read more.
This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies. Full article
(This article belongs to the Special Issue Financial Modeling)
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Review

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27 pages, 662 KiB  
Review
Parametric Estimation of Diffusion Processes: A Review and Comparative Study
by Alejandra López-Pérez, Manuel Febrero-Bande and Wencesalo González-Manteiga
Mathematics 2021, 9(8), 859; https://doi.org/10.3390/math9080859 - 14 Apr 2021
Cited by 2 | Viewed by 2436
Abstract
This paper provides an in-depth review about parametric estimation methods for stationary stochastic differential equations (SDEs) driven by Wiener noise with discrete time observations. The short-term interest rate dynamics are commonly described by continuous-time diffusion processes, whose parameters are subject to estimation bias, [...] Read more.
This paper provides an in-depth review about parametric estimation methods for stationary stochastic differential equations (SDEs) driven by Wiener noise with discrete time observations. The short-term interest rate dynamics are commonly described by continuous-time diffusion processes, whose parameters are subject to estimation bias, as data are highly persistent, and discretization bias, as data are discretely sampled despite the continuous-time nature of the model. To assess the role of persistence and the impact of sampling frequency on the estimation, we conducted a simulation study under different settings to compare the performance of the procedures and illustrate the finite sample behavior. To complete the survey, an application of the procedures to real data is provided. Full article
(This article belongs to the Special Issue Financial Modeling)
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