Technology, Digital Transformation, and Financial Economics

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (1 April 2024) | Viewed by 8252

Special Issue Editor


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Guest Editor
Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University, Corpus Christi, TX 78412, USA
Interests: asset pricing; banking; blockchain; computational finance; data analytics; fintech
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Special Issue Information

Dear Colleagues,

The accelerating digital transformation of the 21st century has resulted in fundamental changes in our global business landscape and the speed and methods in which payments are made. These steps forward have been coupled the evolution of industries, changes in the ways in which they connect with customers, and the growth in inter-linkages between cultures, societies, nations, governments and firms. Advances in technology are also met with a growing call for appropriate regulatory measures and oversight to protect investors, customers, and individuals.

The aim of this Special Issue of Risks is to create a multidisciplinary discussion platform and to invite scholars to debate critical questions that extend knowledge at the intersection of technology, the digital economic transformation, and the various fields within financial economics. This Special Issue welcomes authors to submit research (original research and approaches, perspectives, ideas) that focuses on topics including, but not limited to, the following issues:

  • Big data analytics and their applications;
  • Digital computational tools, such as machine learning, artificial intelligence (AI), and virtual reality (VR);
  • Financial economic risks and our digital economy;
  • The development of risk models and their application to our digital economy;
  • Cryptocurrencies, FinTech, and digital money;
  • Central Bank Digital Currencies (CBDCs) and their risks and benefits to society;
  • Digital transformation and corporate social responsibility (CSR);
  • Mobile internet, blockchain, internet of things (IoT);
  • E-commerce and (social) network platforms;
  • Legal and regulatory measures and responses in our digital economy;
  • Public policy and societal implications of digital transformation;
  • Digitization of our economy and energy.

Dr. Dimitrios Koutmos
Guest Editor

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Keywords

  • digital transformation
  • digital economy
  • FinTech
  • cryptocurrencies
  • blockchain
  • e-commerce and the Internet
  • network platforms
  • risk management
  • technology
  • social responsibility
  • energy economics

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Published Papers (3 papers)

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Research

13 pages, 1597 KiB  
Article
Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility
by Rhenan G. S. Queiroz and Sergio A. David
Risks 2023, 11(12), 211; https://doi.org/10.3390/risks11120211 - 6 Dec 2023
Cited by 4 | Viewed by 2560
Abstract
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, [...] Read more.
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered. Full article
(This article belongs to the Special Issue Technology, Digital Transformation, and Financial Economics)
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18 pages, 997 KiB  
Article
Cryptocurrency Trading and Downside Risk
by Farhat Iqbal, Mamoona Zahid and Dimitrios Koutmos
Risks 2023, 11(7), 122; https://doi.org/10.3390/risks11070122 - 6 Jul 2023
Cited by 2 | Viewed by 3260
Abstract
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, [...] Read more.
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies. Full article
(This article belongs to the Special Issue Technology, Digital Transformation, and Financial Economics)
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17 pages, 910 KiB  
Article
Using US Stock Sectors to Diversify, Hedge, and Provide Safe Havens for NFT Coins
by Perry Sadorsky and Irene Henriques
Risks 2023, 11(7), 119; https://doi.org/10.3390/risks11070119 - 29 Jun 2023
Viewed by 1507
Abstract
This paper explores risk management strategies for investments in Nonfungible Token (NFT) coins through their diversification within the S&P 500 industry sectors. Given the significant decline in NFT coin values in 2022, understanding these strategies is critical for investors. This study focused on [...] Read more.
This paper explores risk management strategies for investments in Nonfungible Token (NFT) coins through their diversification within the S&P 500 industry sectors. Given the significant decline in NFT coin values in 2022, understanding these strategies is critical for investors. This study focused on four major NFT coins (Enjin coin (ENJ), MANA, Theta coin (THETA), and the Tezos coin (XTZ)) and employed ETFs representing the major S&P 500 sectors for analysis. Dynamic conditional correlation GARCH models have been used, to estimate correlations between the NFT coins and US industry sector ETFs. Our findings showed that while most S&P 500 sectors offered diversification benefits in the pre-COVID period, all of them did during the COVID period. However, these sectors are generally weak safe havens and poor hedges. Portfolio analysis suggests an optimal NFT coin weighting of 10–30%, based on the Sharpe ratio. This study aims to pave the way for informed decision-making in the dynamic NFT market. Full article
(This article belongs to the Special Issue Technology, Digital Transformation, and Financial Economics)
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