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Mathematics
Special Issues
Advanced Statistical Applications in Financial Econometrics
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Special Issue "Advanced Statistical Applications in Financial Econometrics"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 584

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 Mathematics. 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.

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. 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 2100 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 (1 paper)

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Research

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Article
An Adaptive Multiple-Asset Portfolio Strategy with User-Specified Risk Tolerance
by
Yufeng Lin
,
Xiaogang Wang
and
Yuehua Wu
Mathematics 2023, 11(7), 1637; https://doi.org/10.3390/math11071637 - 28 Mar 2023
Viewed by 423
Abstract
We improve the traditional simple moving average strategy by incorporating an investor-specific risk tolerance into the method. We then propose a multiasset generalized moving average crossover (MGMA) strategy. The MGMA strategies allocate wealth between risky assets and risk-free assets in an adaptive manner, [...] Read more.
We improve the traditional simple moving average strategy by incorporating an investor-specific risk tolerance into the method. We then propose a multiasset generalized moving average crossover (MGMA) strategy. The MGMA strategies allocate wealth between risky assets and risk-free assets in an adaptive manner, with the risk tolerance specified by an investor. We derive the expected log-utility of wealth, which allows us to estimate the optimal allocation parameters. The algorithm using our MGMA strategy is also presented. As the multiple risky assets can have different variability levels and could have various degrees of correlations, this trading strategy is evaluated on both simulated data and global high-frequency exchange-traded fund (ETF) data. It is shown that the MGMA strategies could significantly increase both the investor’s expected utility of wealth and the investor’s expected wealth. Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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