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Feature Paper Special Issue: Quantitative Finance
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Feature Paper Special Issue: Quantitative Finance

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 14313

Special Issue Editors

Prof. Dr. Gareth W. Peters

E-Mail Website
Guest Editor
School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: financial risk management and insurance; actuarial machine learning methodology; time series and state-space modelling; spatial statistics; stochastic processes in financial applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Damien Challet

E-Mail Website
Guest Editor
Laboratoire de Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
Interests: agent behaviour; statistics; network inference; machine learning applied to financial data and fundamental economic data
Prof. Dr. Hongsong Yuan

E-Mail Website
Guest Editor
School of Information Management & Engineering, Shanghai University of Finance and Economics (SUFE), Shanghai 200433, China
Interests: statistics; financial engineering
Prof. Dr. Paweł Polak

E-Mail Website
Guest Editor
Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
Interests: time series analysis; statistics; machine learning; portfolio optimization; risk management
Prof. Dr. Min Shu

E-Mail Website
Guest Editor
Department of Mathematics, Statistics and Computer Science, University of Wisconsin-Stout, Menomonie, WI 54751, USA
Interests: applied statistics; quantitative finance; time series analysis; machine learning; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are publishing a special issue on Quantitative Finance. Through this issue, we aim to attract papers covering the myriad facets in the analysis and modeling of financial data, events, and phenomena, including:

  • Statistical analysis and modeling
  • Machine learning methods
  • Mathematical modeling
  • Statistical physics modeling
  • Optimization algorithms
  • Other relevant methods

Timewise, we shall accept papers from as early as September 2021, until 1 March 2022. All qualified papers passing the rigorous peer-review process will be published online, free of charge, and with open access. Qualified papers will be published sequentially in this Special Issue. This way, early submissions can be published without delay. We are looking forward to your submission in this increasingly important field.

Prof. Dr. Gareth W. Peters
Prof. Dr. Damien Challet
Prof. Dr. Hongsong Yuan
Prof. Dr. Paweł Polak
Prof. Dr. Min Shu
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. Stats is an international peer-reviewed open access quarterly 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 1600 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

  • statistical analysis and modeling
  • machine learning methods
  • mathematical modeling
  • statistical physics modeling
  • optimization algorithms
  • other relevant methods

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

7 pages, 423 KiB  
Article
Has the Market Started to Collapse or Will It Resist?
by Yao Kuang and Raphael Douady
Stats 2022, 5(2), 401-407; https://doi.org/10.3390/stats5020023 - 23 Apr 2022
Cited by 1 | Viewed by 6295
Abstract
Many people are concerned about the stock market in 2022 as it faces several threats, from rising inflation rates to geopolitical events. The S&P 500 Index has already dropped about 10% from the peak in early January 2022 until the end of February [...] Read more.
Many people are concerned about the stock market in 2022 as it faces several threats, from rising inflation rates to geopolitical events. The S&P 500 Index has already dropped about 10% from the peak in early January 2022 until the end of February 2022. This paper aims at updating the crisis indicator to predict when the market may experience a significant drawdown, which we developed in Crisis Risk Prediction with Concavity from Polymodel (2022). This indicator uses regime switching and Polymodel theory to calculate the market concavity. We found that concavity had not increased in the past 6 months. We conclude that at present, the market does not bear inherent dynamic instability. This does not exclude a possible collapse which would be due to external events unrelated to financial markets. Full article
(This article belongs to the Special Issue Feature Paper Special Issue: Quantitative Finance)
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21 pages, 3577 KiB  
Article
The 2021 Bitcoin Bubbles and Crashes—Detection and Classification
by Min Shu, Ruiqiang Song and Wei Zhu
Stats 2021, 4(4), 950-970; https://doi.org/10.3390/stats4040056 - 21 Nov 2021
Cited by 14 | Viewed by 7521
Abstract
In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model [...] Read more.
In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study. Full article
(This article belongs to the Special Issue Feature Paper Special Issue: Quantitative Finance)
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