Modelling and Analysis in Time Series and Econometrics

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6314

Special Issue Editors


E-Mail Website
Guest Editor
1. School of Business and Industry, Florida A&M University, Tallahassee, FL, USA
2. Department of Scientific Computing, Florida State University, Tallahassee, FL, USA
Interests: time series analysis; operations management

E-Mail Website
Guest Editor
Department of Mathematics, Florida A and M University, Tallahassee, FL 32307, USA
Interests: applied mathematics

Special Issue Information

Dear Colleagues,

We are pleased to announce the forthcoming Special Issue, “Modelling and Analysis in Time Series and Econometrics”. Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. This subject matter aims to highlight the role of probability and mathematical statistics in solving problems in planning microeconomic and macroeconomic systems in society. Potential applications may be concerned with, but are not limited to, finance, financial mathematics, technology, energy, industry, environment, sustainability, medicine, biology, agriculture, and climate change. This Special Issue will include both papers that develop new methodologies as well as those using known procedures for working out real-world problems. In addition to original research papers, review articles are also welcome.

With this Special Issue, Mathematics also wants to promote a new generation of researchers in probability and statistics, attracting student interest to this field.

We believe that your contributions will be highly valuable to make this Special Issue a great success. We look forward to receiving your papers!

Prof. Dr. Dennis Ridley
Prof. Dr. Pierre Ngnepieba
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

  • mathematical modeling
  • time series analysis
  • antithetic time series
  • time series forecasting
  • cointegration
  • antithetic variables in computer simulation
  • sequences of random variables
  • stochastic processes
  • bias reduction
  • dynamics
  • mathematical statistics
  • econometrics
  • economic theory
  • optimization

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 1385 KiB  
Article
A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression
by Yumei Ren, Guoqiang Tang, Xin Li and Xuchang Chen
Mathematics 2023, 11(16), 3502; https://doi.org/10.3390/math11163502 - 14 Aug 2023
Viewed by 1340
Abstract
In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select [...] Read more.
In the data-driven era, the mining of financial asset information and the selection of appropriate assets are crucial for stable returns and risk control. Multifactor quantitative models are a common method for stock selection in financial assets, so it is important to select the optimal set of factors. Elastic Net, which combines the benefits of the L1 and L2 penalty terms, performs better at filtering features due to the complexity of the features in high-dimensional datasets than Lasso and Ridge regression. At the same time, the false discovery rate (FDR), which is important for making reliable investment decisions, is not taken into account by the current factor-selection methodologies. Therefore, this paper constructs the Knockoff Logistic regression Elastic Net (KF-LR-Elastic Net): combining Logistic regression with Elastic Net and using Knockoff to control the FDR of variable selection to achieve factor selection. Based on the selected factors, stock returns are predicted under Logistic regression. The overall model is denoted as Knockoff Logistic regression Elastic Net-Logistic regression (KL-LREN-LR). The empirical study is conducted with data on the CSI 300 index constituents in the Chinese market from 2016–2022. KF-LREN-LR is used for factor selection and stock-return forecasting to select the top 10 stocks and establish an investment strategy for daily position changing. According to empirical evidence, KF-LR-Elastic Net can select useful factors and control the FDR, which is helpful for increasing the accuracy of factor selection. The KF-LREN-LR forecast portfolio has the advantages of high return and controlled risk, so it is informative for optimizing asset allocation. Full article
(This article belongs to the Special Issue Modelling and Analysis in Time Series and Econometrics)
Show Figures

Figure 1

24 pages, 1861 KiB  
Article
Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model
by Tsegai O. Yhdego, An-Tsun Wei, Gordon Erlebacher, Hui Wang and Miguel G. Tejada
Mathematics 2023, 11(11), 2427; https://doi.org/10.3390/math11112427 - 24 May 2023
Viewed by 2466
Abstract
Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving [...] Read more.
Some delay patterns are correlated to historical performance and can reflect the trend of delays in future flights. A typical example is the delay from an earlier inbound flight causing delayed departure of a connecting and downstream outbound flight. Specifically, if an arriving aircraft arrives late, the connecting airline may decide to wait for connecting passengers. Due to the consistent flow of passengers to various destinations during a travel season, similar delay patterns could occur in future days/weeks. Airlines may analyze such trends days or weeks before flights to anticipate future delays and redistribute resources with different priorities to serve those outbound flights that are likely to be affected by feeder delays. In this study, we use a hybrid recurrent neural network (RNN) model to estimate delays and project their impacts on downstream flights. The proposed model integrates a gated recurrent unit (GRU) model to capture the historical trend and a dense layer to capture the short-term dependency between arrival and departure delays, and, then, integrates information from both branches using a second GRU model. We trained and tuned the model with data from nine airports in North, Central, and South America. The proposed model outperformed alternate approaches with traditional structures in the testing phase. Most of the predicted delay of the proposed model were within the predefined 95% confidence interval. Finally, to provide operational benefits to airline managers, our analysis measured the future impact of a potentially delayed inbound feeder, (PDIF) in a case study, by means of identifying the outbound flights which might be affected based on their available connection times (ACTs). From an economic perspective, the proposed algorithm offers potential cost savings for airlines to prevent or minimize the impact of delays. Full article
(This article belongs to the Special Issue Modelling and Analysis in Time Series and Econometrics)
Show Figures

Graphical abstract

16 pages, 435 KiB  
Article
Nonparametric Threshold Estimation for Drift Function in Jump–Diffusion Model of Interest Rate Using Asymmetric Kernel
by Yuping Song, Chen Li, Hemin Wang, Jiayi Meng and Liang Hao
Mathematics 2023, 11(10), 2281; https://doi.org/10.3390/math11102281 - 13 May 2023
Viewed by 996
Abstract
The existing estimators for the drift coefficient in the diffusion model with jumps involve jump components and possess larger boundary error. How to effectively estimate the drift function is an important issue that faces challenges and has theoretical significance. In this paper, the [...] Read more.
The existing estimators for the drift coefficient in the diffusion model with jumps involve jump components and possess larger boundary error. How to effectively estimate the drift function is an important issue that faces challenges and has theoretical significance. In this paper, the gamma asymmetric kernel for boundary correction and threshold function eliminating jump impacts are combined to estimate the unknown drift coefficient in the jump diffusion process of interest rate. The asymptotic large sample property and the better finite sample property through the Monte Carlo numerical simulation experiment and the empirical analysis of SHIBOR and LIBOR for the corresponding estimator are considered in detail. It is found that the estimator proposed in this paper can correct the estimation error near or far away from the origin point, which provides a more asymptotic unbiased estimator for the drift function in diffusion models with jumps. Full article
(This article belongs to the Special Issue Modelling and Analysis in Time Series and Econometrics)
Show Figures

Figure 1

12 pages, 2790 KiB  
Article
Antithetic Power Transformation in Monte Carlo Simulation: Correcting Hidden Errors in the Response Variable
by Dennis Ridley and Pierre Ngnepieba
Mathematics 2023, 11(9), 2097; https://doi.org/10.3390/math11092097 - 28 Apr 2023
Cited by 1 | Viewed by 892
Abstract
Monte Carlo simulation is performed with uniformly distributed U(0,1) pseudo-random numbers. Because the numbers are generated from a mathematical formula, they will contain some serial correlation, even if very small. This serial correlation becomes embedded in the correlation structure of the response variable. [...] Read more.
Monte Carlo simulation is performed with uniformly distributed U(0,1) pseudo-random numbers. Because the numbers are generated from a mathematical formula, they will contain some serial correlation, even if very small. This serial correlation becomes embedded in the correlation structure of the response variable. The response variable becomes an asynchronous time series. This leads to hidden errors in the response variable. The purpose of this paper is to illustrate how this happens and how it can be corrected. The method is demonstrated for the case of a simple queue for which the time in the system is known exactly from theory. The paper derives the correlation between an exponential random variable and its antithetic counterpart obtained by power transform with an infinitesimal negative exponent. Full article
(This article belongs to the Special Issue Modelling and Analysis in Time Series and Econometrics)
Show Figures

Figure 1

Back to TopTop