Digital and Conventional Assets (2nd Edition)

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: 1 March 2025 | Viewed by 1255

Special Issue Editor


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Guest Editor
Department of Accounting and Finance, Applied Science University, East Al-Ekir 5055, Bahrain
Interests: REIT markets; spillovers; DeFi; currencies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of digital assets has been topical in many conversations in finance. There is a common belief that these assets will boost liquidity and fundraising, enrich global investor pools, and impact other financial markets. With the change in the dynamics of global financial markets, digital and conventional assets may provide evidence of responsive behavior to various global economic events. This implies that both digital and traditional assets may exhibit similar reactions or correlations to economic factors such as changes in interest rates, geopolitical events, or macroeconomic indicators. Understanding these relationships is crucial for investors and policymakers to make informed decisions and manage risks effectively. This suggests that there is ongoing research and academic interest in exploring the advancements and interactions between digital finance and traditional financial systems.

This Special Issue aims to provide selected contributions on advances in digital finance and their relationships with conventional assets.

Dr. Ramzi Nekhili
Guest Editor

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Keywords

  • risk management in digital finance
  • decentralized financial assets
  • tokenized securities
  • regulatory challenges and opportunities
  • financial inclusion and digital finance
  • market integration
  • spillovers
  • interplay between digital and conventional payment system
  • investor behavior in digital finance
  • technological innovation in digital finance

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Published Papers (1 paper)

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Research

14 pages, 865 KiB  
Article
Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data
by Kostas Giannopoulos, Ramzi Nekhili and Christos Christodoulou-Volos
Int. J. Financial Stud. 2024, 12(4), 99; https://doi.org/10.3390/ijfs12040099 - 8 Oct 2024
Viewed by 940
Abstract
Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for [...] Read more.
Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades. Full article
(This article belongs to the Special Issue Digital and Conventional Assets (2nd Edition))
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