On the Prediction of Systemic Risk Tolerance of Cryptocurrencies

Sabri Boubaker, Sitara Karim, Muhammad Abubakr Naeem and Molla Ramizur Rahman
Additional contact information
Sitara Karim: Sunway University [Malaysia]
Muhammad Abubakr Naeem: Swansea University

Post-Print from HAL

Abstract: The role of big data in finance is pivotal, especially in forecasting stock prices, mitigating risk, and assessing market anomalies. With the financial system becoming more interconnected, analytical models using large data are gaining prominence in developing risk spillover models. This study estimates the systemic risk tolerance of twenty-five high-valued cryptocurrencies and finds that Fantom has the highest tolerance, while Bitcoin and Ethereum have a lower tolerance due to their large market share. It also shows that the common trend of cryptocurrencies enhances each other's tolerance and develops a predictive model for systemic risk tolerance. The study can help investors and market participants devise strategies for safe haven investment, hedging, and speculation during a market downturn. \textcopyright 2023 Elsevier Inc.

Keywords: Bitcoin; Commonality; Competition; Crisis; Cryptocurrency; currency; Financial markets; financial system; Financial system; Forecasting stock prices; Investments; Large data; Market share; Mitigating risk; prediction; Risk assessment; Risk tolerance; stock market; Systemic risk tolerance; Systemic risks (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Published in Technological Forecasting and Social Change, 2024, 198, ⟨10.1016/j.techfore.2023.122963⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: On the prediction of systemic risk tolerance of cryptocurrencies (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04432400

DOI: 10.1016/j.techfore.2023.122963

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().