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Mathematical and Computational Applications in Finance and Economics
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Mathematical and Computational Applications in Finance and Economics

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Social Sciences".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9276

Special Issue Editors

Prof. Dr. Wing-Keung Wong

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Guest Editor
1. Department of Finance, Fintech & Blockchain Research Center, Big Data Research Center, Asia University, Taichung City 41354, Taiwan
2. Department of Medical Research, China Medical University Hospital, Taichung City 40447, Taiwan
3. Department of Economics and Finance, The Hang Seng University of Hong Kong, Hong Kong, China
Interests: behavioral models; mathematical modeling; econometrics; energy economics; equity analysis; investment theory; risk management; behavioral economics; operational research; decision theory; environmental economics; public health; time series analysis; forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moawia Alghalith

E-Mail Website
Guest Editor
Department of Economics, University of West Indies, St. Augustine, Trinidad, Trinidad and Tobago
Interests: stochastic analysis; PDEs; volatility; optimization; the portfolio models; options
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aviral Kumar Tiwari

E-Mail Website
Guest Editor
Rajagiri Business School, Rajagiri Valley Campus, Kochi 682039, India
Interests: energy and environmental economics, tourism, cryptocurrencies, applied econometrics (linear and non-linear time series and panel data techniques); applied macroeconomics; open economy macroeconomics; public finance and fiscal policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematical and computational models are often developed by using advanced mathematics, probability, and statistics. Mathematical and computational models are essential to develop theories and tools in finance and economics to test their validity through the analysis of empirical real-world data.

A Special Issue, “Mathematical and Computational Applications in Finance and Economics”, edited by Wing-Keung Wong, Moawia Alghalith, and Aviral Kumar Tiwari, will be devoted to advancements in the mathematical and computational models with applications in different areas of finance and economics. This Special Issue will also bring together practical, state-of-the-art applications of mathematics, probability, statistics, and computational techniques in these areas.

We invite investigators to contribute original research articles that advance the use of mathematics, probability, statistics, and computational techniques in the areas of finance and economics. All submissions must contain original unpublished work not being considered for publication elsewhere.

Prof. Dr. Wing-Keung Wong
Prof. Dr. Moawia Alghalith
Prof. Dr. Aviral Kumar Tiwari
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. Mathematical and Computational Applications 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 1400 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

  • mathematics
  • probability
  • statistics
  • computation
  • finance
  • economics

Published Papers (4 papers)

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Research

24 pages, 2458 KiB  
Article
How Do Financial Development and Renewable Energy Affect Consumption-Based Carbon Emissions?
by Abraham Ayobamiji Awosusi, Tomiwa Sunday Adebayo, Husam Rjoub and Wing-Keung Wong
Math. Comput. Appl. 2022, 27(4), 73; https://doi.org/10.3390/mca27040073 - 22 Aug 2022
Cited by 8 | Viewed by 1787
Abstract
This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-based carbon emissions (CCO2e) in Uruguay. [...] Read more.
This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-based carbon emissions (CCO2e) in Uruguay. Our study incorporates other drivers of CCO2 emissions, such as financial development and renewable energy, into the model. We find that, in the majority of the quantiles, exports, financial development, and renewable energy exert a negative impact on CCO2e, and the influence of imports on CCO2e is positive in all quantiles. Moreover, the quantile regression approach is used as a robustness test for the quantile-on-quantile approach. The causal interaction from the regressors to CCO2e is evaluated using the nonparametric Granger causality test in quantiles. The outcome of the nonparametric Granger causality test in quantiles suggests that imports, exports, renewable energy, and financial development can predict CCO2e at different quantiles. Based on these outcomes, we recommend that the financial sector must strengthen its focus on giving funding to enterprises that embrace environmentally friendly technologies and incentivize them to employ other energy-efficient technologies for manufacturing reasons, thereby preventing environmental deterioration. Full article
(This article belongs to the Special Issue Mathematical and Computational Applications in Finance and Economics)
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18 pages, 1115 KiB  
Article
On the Elicitability and Risk Model Comparison of Emerging Markets Equities
by Peterson Owusu Junior, Imhotep Paul Alagidede and Aviral Kumar Tiwari
Math. Comput. Appl. 2021, 26(3), 63; https://doi.org/10.3390/mca26030063 - 06 Sep 2021
Cited by 2 | Viewed by 2114
Abstract
The need for comparative backtesting in the Basel III framework presents the challenge for ranking of internal value-at-risk (VaR) and expected shortfall (ES) models. We use a joint loss function to score the elicitable joint VaR and ES models to select competing tail [...] Read more.
The need for comparative backtesting in the Basel III framework presents the challenge for ranking of internal value-at-risk (VaR) and expected shortfall (ES) models. We use a joint loss function to score the elicitable joint VaR and ES models to select competing tail risk models for the top 9 emerging markets equities and the emerging markets composite index. We achieve this with the model confidence set (MCS) procedure. Our analysis span two sub-sample periods representing turbulent (Eurozone and Global Financial crises periods) and tranquil (post-Global Financial crisis period) market conditions. We find that many of the markets risk models are time-invariant and independent of market conditions. But for China and South Africa this is not true because their risk models are time-varying, market conditions-dependent, percentile-dependent and heterogeneous. Tail risk modelling may be difficult compared to other markets. The resemblance between China and South Africa can stem from the closeness between their equities composition. However, generally, there is evidence of more homogeneity than heterogeneity in risk models. This is indicated by a minimum of three models (out of six) per equity in most of the countries. This may ease the burden for risk managers to find the optimal set of models. Our study is important for internal risk modelling, regulatory oversight, reduce regulatory arbitrage and may bolster confidence in international investors with respect to emerging markets equities. Full article
(This article belongs to the Special Issue Mathematical and Computational Applications in Finance and Economics)
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13 pages, 665 KiB  
Article
Uncertainty, Spillovers, and Forecasts of the Realized Variance of Gold Returns
by Rangan Gupta and Christian Pierdzioch
Math. Comput. Appl. 2021, 26(3), 49; https://doi.org/10.3390/mca26030049 - 02 Jul 2021
Viewed by 1966
Abstract
Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance of gold returns and its upside (good) and downside (bad) [...] Read more.
Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance of gold returns and its upside (good) and downside (bad) counterparts. We go beyond earlier research in that we do not focus exclusively on U.S.-based measures of uncertainty, and in that we account for international spillovers of uncertainty. Our results, based on the Lasso estimator, show that, across the various model configurations that we study, uncertainty has a more systematic effect on out-of-sample forecast accuracy than spillovers. Our results have important implications for investors in terms of, for example, pricing of related derivative securities and the development of portfolio-allocation strategies. Full article
(This article belongs to the Special Issue Mathematical and Computational Applications in Finance and Economics)
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16 pages, 1422 KiB  
Article
A Front-Fixing Implicit Finite Difference Method for the American Put Options Model
by Riccardo Fazio, Alessandra Insana and Alessandra Jannelli
Math. Comput. Appl. 2021, 26(2), 30; https://doi.org/10.3390/mca26020030 - 13 Apr 2021
Cited by 2 | Viewed by 2329
Abstract
In this paper, we present an implicit finite difference method for the numerical solution of the Black–Scholes model of American put options without dividend payments. We combine the proposed numerical method by using a front-fixing approach where the option price and the early [...] Read more.
In this paper, we present an implicit finite difference method for the numerical solution of the Black–Scholes model of American put options without dividend payments. We combine the proposed numerical method by using a front-fixing approach where the option price and the early exercise boundary are computed simultaneously. We study the consistency and prove the stability of the implicit method by fixing the values of the free boundary and of its first derivative. We improve the accuracy of the computed solution via a mesh refinement based on Richardson’s extrapolation. Comparisons with some proposed methods for the American options problem are carried out to validate the obtained numerical results and to show the efficiency of the proposed numerical method. Finally, by using an a posteriori error estimator, we find a suitable computational grid requiring that the computed solution verifies a prefixed error tolerance. Full article
(This article belongs to the Special Issue Mathematical and Computational Applications in Finance and Economics)
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