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Entropy
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Advanced Statistical Applications in Financial Econometrics
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Advanced Statistical Applications in Financial Econometrics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2002

Special Issue Editors

Prof. Dr. Yuehua Wu

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
Interests: statistical modeling and inference for data with a very complex structure and/or with high dimension
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Baisuo Jin

E-Mail Website
Guest Editor
Department Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
Interests: Bayesian methods; change point analysis; large dimensional random matrix; model selection; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are welcome to make contributions to this Special Issue on “Advanced Statistical Applications in Financial Econometrics” in the journal Entropy. The field of financial econometrics is very broad and complex. Many challenging problems emerge as technology advances. This is a research area that has attracted the attention of an increasing number of researchers in recent years. This special issue will emphasize original contributions addressing challenges in advanced statistical applications in financial econometrics, including regime-switching modeling, portfolio optimization, asset allocation, risk analysis, financial contagion analysis, machine learning, and stochastic process models.

You may choose our Joint Special Issue in Mathematics.

Prof. Dr. Yuehua Wu
Prof. Dr. Baisuo Jin
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. Entropy is an international peer-reviewed open access monthly 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 econometrics
  • risk analysis
  • financial contagion analysis
  • change-point analysis
  • regime-switching modeling
  • portfolio optimization
  • asset allocation
  • machine learning
  • stochastic process models
  • Markov chain/process

Published Papers (2 papers)

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Research

22 pages, 359 KiB  
Article
Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets
by Rodrigo Colnago Contreras, Vitor Trevelin Xavier da Silva, Igor Trevelin Xavier da Silva, Monique Simplicio Viana, Francisco Lledo dos Santos, Rodrigo Bruno Zanin, Erico Fernandes Oliveira Martins and Rodrigo Capobianco Guido
Entropy 2024, 26(3), 177; https://doi.org/10.3390/e26030177 - 20 Feb 2024
Viewed by 770
Abstract
Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily [...] Read more.
Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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29 pages, 422 KiB  
Article
The Financial Risk Measurement EVaR Based on DTARCH Models
by Xiaoqian Liu, Zhenni Tan, Yuehua Wu and Yong Zhou
Entropy 2023, 25(8), 1204; https://doi.org/10.3390/e25081204 - 13 Aug 2023
Cited by 1 | Viewed by 865
Abstract
The value at risk based on expectile (EVaR) is a very useful method to measure financial risk, especially in measuring extreme financial risk. The double-threshold autoregressive conditional heteroscedastic (DTARCH) model is a valuable tool in assessing the volatility of a financial asset’s return. [...] Read more.
The value at risk based on expectile (EVaR) is a very useful method to measure financial risk, especially in measuring extreme financial risk. The double-threshold autoregressive conditional heteroscedastic (DTARCH) model is a valuable tool in assessing the volatility of a financial asset’s return. A significant characteristic of DTARCH models is that their conditional mean and conditional variance functions are both piecewise linear, involving double thresholds. This paper proposes the weighted composite expectile regression (WCER) estimation of the DTARCH model based on expectile regression theory. Therefore, we can use EVaR to predict extreme financial risk, especially when the conditional mean and the conditional variance of asset returns are nonlinear. Unlike the existing papers on DTARCH models, we do not assume that the threshold and delay parameters are known. Using simulation studies, it has been demonstrated that the proposed WCER estimation exhibits adequate and promising performance in finite samples. Finally, the proposed approach is used to analyze the daily Hang Seng Index (HSI) and the Standard & Poor’s 500 Index (SPI). Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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