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Econometrics of Financial Models and Market Microstructure
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Econometrics of Financial Models and Market Microstructure

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 6527

Special Issue Editors

Prof. Dr. David Allen

E-Mail Website
Guest Editor
1. School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
2. School of Business and Law, Edith Cowan University, Joondalup, WA 6027, Australia
3. Department of Finance, Asia University, Wufeng 41354, Taiwan
Interests: financial econometrics; financial economics; time-series; investments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shelton Peiris

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
Interests: statistical analysis of stationary and non-stationary time series data; theory and applications of estimating functions; financial time series modelling; saddle point and Edgeworth type approximations related to time series problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are planning a Special Issue on the theme of the  "Econometrics of Financial Models and Market Microstructure". 

The rapid advances in 'big data' availability, innovations in financial modelling and econometric techniques, machine learning and artificial intelligence capabilities, plus continuous financial innovation,  combine to provide a great deal of scope for novel research in the various abovementioned topic areas. For example,  in recent developments in financial econometrics  and in the analysis of risk, there has been a move towards non-linear and non-parametric modelling which moves away from the standard adoption of Gauss–Markov assumptions. The use of the machinery of copula analysis becomes ever more flexible. 

The advancement and adoption of innovations such as blockchain technology and the expansion of cryptocurrency markets have served to make these markets more mainstream and to promote their greater adoption by investment managers and financial institutions. Market microstructure research has also been supported by the explosion of information channels, such as online bulletin boards, social media, etc., which have fostered greater application of data mining exercise, machine learning techniques, and sentiment analysis. This Special Issue will welcome papers on one or more of these topics.

Prof. Dr. David Allen
Prof. Dr. Shelton Peiris
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. Journal of Risk and Financial Management 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 1400 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

  • novel econometric models
  • machine learning and AI
  • non-linear and non-parametric
  • blockchain and cryptocurrencies
  • data mining and sentiment analysis
  • recent developments in time series methods

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Published Papers (2 papers)

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Research

8 pages, 766 KiB  
Article
A New Measure for Idiosyncratic Risk Based on Decomposition Method
by Meng-Horng Lee, Chee-Wooi Hooy and Robert Brooks
J. Risk Financial Manag. 2023, 16(1), 43; https://doi.org/10.3390/jrfm16010043 - 9 Jan 2023
Cited by 1 | Viewed by 2831
Abstract
This paper introduces an alternate measure of idiosyncratic risk leveraged from the decomposition method to further eliminate the residual systematic risk inherent in the factor asset pricing model. Combining both complementary techniques contributes to a more comprehensive firm-level idiosyncratic risk that is crucial [...] Read more.
This paper introduces an alternate measure of idiosyncratic risk leveraged from the decomposition method to further eliminate the residual systematic risk inherent in the factor asset pricing model. Combining both complementary techniques contributes to a more comprehensive firm-level idiosyncratic risk that is crucial in both portfolio diversification and alpha investing. We focus our result on the idiosyncratic risk estimations and their behaviour on 36 emerging markets covering 39 industries. We show that the new measure exhibits a declining trend across time, consistent with the fact that emerging markets are becoming more integrated with the increased level of common effect across time. Full article
(This article belongs to the Special Issue Econometrics of Financial Models and Market Microstructure)
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16 pages, 24078 KiB  
Article
Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures
by Sulalitha Bowala and Japjeet Singh
J. Risk Financial Manag. 2022, 15(10), 427; https://doi.org/10.3390/jrfm15100427 - 25 Sep 2022
Cited by 7 | Viewed by 2581
Abstract
Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. [...] Read more.
Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio. Full article
(This article belongs to the Special Issue Econometrics of Financial Models and Market Microstructure)
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