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Mathematics
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Advanced Statistical Applications in Financial Econometrics
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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: 30 September 2024 | Viewed by 4220

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 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 (4 papers)

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Research

20 pages, 3548 KiB  
Article
Dynamic Asymmetric Volatility Spillover and Connectedness Network Analysis among Sectoral Renewable Energy Stocks
by Hleil Alrweili and Ousama Ben-Salha
Mathematics 2024, 12(12), 1816; https://doi.org/10.3390/math12121816 - 11 Jun 2024
Viewed by 370
Abstract
A wide range of statistical and econometric models have been applied in the extant literature to compute and assess the volatility spillovers among renewable stock prices. This research adds to the body of knowledge by analyzing the dynamic asymmetric volatility spillover between major [...] Read more.
A wide range of statistical and econometric models have been applied in the extant literature to compute and assess the volatility spillovers among renewable stock prices. This research adds to the body of knowledge by analyzing the dynamic asymmetric volatility spillover between major NASDAQ OMX Green Economy Indices, including solar, wind, geothermal, fuel cell, and developer/operator. The novelty of the research is that it distinguishes between positive and negative volatility spillovers in a time-varying fashion and conducts a connectedness network analysis. To do so, the study implements the Time-Varying Parameter Vector Autoregression (TVP-VAR) approach, as well as the connectedness network. The empirical investigation is based on high-frequency data between 18 October 2010, and 2 April 2022. The main findings may be summarized as follows. First, the analysis reveals a shift in the dominance of positive and negative volatility transmission during the study period, which represents compelling evidence of dynamic asymmetric spillover in the volatility transmission between renewable energy stocks. Second, the connectedness analysis indicates that the operator/developer and solar sectors are the net transmitters of both positive and negative volatility to the system. In contrast, the wind, geothermal and fuel cell sectors receive shocks from other renewable energy stocks. The asymmetric spillovers between the renewable energy stocks are confirmed using the block bootstrapping technique. Finally, the dynamic analysis reveals a substantial impact of the COVID-19 outbreak on the interdependence between renewable energy stocks. The findings above are robust to different lag orders and prediction ranges. Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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21 pages, 739 KiB  
Article
A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices
by Tim Leung and Theodore Zhao
Mathematics 2024, 12(6), 864; https://doi.org/10.3390/math12060864 - 15 Mar 2024
Viewed by 812
Abstract
We analyze the multiscale behaviors of high-frequency intraday prices, with a focus on how asset prices are correlated over different timescales. The multiscale approach proposed in this paper is designed for the analysis of high-frequency intraday prices. It incorporates microstructure noise into the [...] Read more.
We analyze the multiscale behaviors of high-frequency intraday prices, with a focus on how asset prices are correlated over different timescales. The multiscale approach proposed in this paper is designed for the analysis of high-frequency intraday prices. It incorporates microstructure noise into the stochastic price process. We consider a noisy fractional Brownian motion model and illustrate its various statistical properties. This leads us to introduce new latent correlation and noise estimators. New numerical algorithms are developed for model estimation using empirical high-frequency data. For a collection of stocks and exchange-traded funds, examples are provided to illustrate the relationship between multiscale correlation and sampling frequency as well as the evolution of multiscale correlation over time. Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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24 pages, 4019 KiB  
Article
Has the COVID-19 Pandemic Led to a Switch in the Volatility of Biopharmaceutical Companies?
by Adriana AnaMaria Davidescu, Eduard Mihai Manta, Oana Mihaela Vacaru (Boita), Mihaela Gruiescu, Razvan Gabriel Hapau and Paul Laurentiu Baranga
Mathematics 2023, 11(14), 3116; https://doi.org/10.3390/math11143116 - 14 Jul 2023
Viewed by 1320
Abstract
Biopharmaceutical companies are critical in developing vaccines, treatments, and diagnostics for COVID-19. Thus, understanding the contagion effects of their stock market can have important economic implications, especially in the context of global financial markets. Due to the COVID-19 pandemic, biopharmaceutical companies’ stock markets [...] Read more.
Biopharmaceutical companies are critical in developing vaccines, treatments, and diagnostics for COVID-19. Thus, understanding the contagion effects of their stock market can have important economic implications, especially in the context of global financial markets. Due to the COVID-19 pandemic, biopharmaceutical companies’ stock markets may have experienced sudden volatility and risk changes, which may have had spillover effects on other sectors and markets. Policymakers can take pre-emptive measures to stabilize financial markets. Analyzing the contagion effects makes it even more relevant to analyze the stock market response of four leading pharmaceutical companies that either developed vaccines against COVID-19 or drugs that help to fight the virus, namely, Pfizer, AbbVie Inc., Sanofi, and Bristol Myers Squibb. The analysis considers two periods, before and during the COVID-19 crisis, and considers the influence of the market volatility and technological market index. In order to capture the contagion effects, DCC-GARCH models have been applied, which estimate time-varying correlation coefficients using a multivariate GARCH framework, allowing for the modeling of time-varying volatility and correlations in financial returns. The results reveal the impact of market volatility on the returns of all four pharmaceutical companies. Additionally, a contagion effect between all four companies, the technological market, and market volatility was observed during the COVID-19 period. Full article
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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35 pages, 895 KiB  
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 1235
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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