Blockchain-Based Asset Securitization and Machine Learning for Finance

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4889

Special Issue Editor


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Guest Editor
Department of Finance, Southern University of Science and Technology, Shenzhen 518055, China
Interests: finance theory; financial engineering; asset pricing; corporate finance; financial contracting; security design; capital structure; real options; asset securitization; machine learning for finance
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Special Issue Information

Dear Colleagues,

The average life span of micro-, small-, and medium-sized enterprises (MSMEs) is only 3–8 years, mainly due to the lack of external financing, especially external equity financing. To solve the problems associated with this, a promising method is to let MSMEs receive financing by issuing asset-backed securities based on blockchain technology. The blockchain technology can record cash flow reliably, verifiably with a timestamp, and distribute wealth intelligently based on the cash flow generated according to the contracts signed in advance.

It is my pleasure to serve as a guest editor of a Special Issue (SI) in the Journal of Risk and Financial Management (ISSN 1911-8074) titled “Blockchain-Based Asset Securitization and Machine Learning for Finance”. The SI includes topics related to contract theory and asset-backed security design and it is better if blockchain technology is taken into account. We are particularly interested in papers where asset-backed security is collateralized by an underlying pool of equity issued by MSMEs subject to financing constraints.

Machine learning in finance is reshaping financial services. Topics for publication include but are not limited to asset pricing, portfolio choice, algorithmic trading, high-frequency trading, fraud detection, loan or insurance underwriting, risk management, and money-laundering prevention.

It will be highly appreciated if you share this post with colleagues who might find this SI to be of interest.

Yours sincerely,

Dr. Zhaojun Yang
Guest Editor

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Keywords

  • asset-backed securitizations
  • blockchain applications in finance
  • financing constraints
  • machine learning for finance
  • algorithmic trading

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Published Papers (1 paper)

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Research

17 pages, 853 KiB  
Article
The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis
by Apostolos Ampountolas
J. Risk Financial Manag. 2023, 16(1), 25; https://doi.org/10.3390/jrfm16010025 - 1 Jan 2023
Cited by 12 | Viewed by 3933
Abstract
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) [...] Read more.
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH). Full article
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