Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis

Abstract

Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic.

Indices on cryptocurrencies: an evaluation

“…Our results are of particular interest to financial engineers and financial regulators: the former can build investment tools on CC indices, such as ETFs or options, while the latter can monitor the CC sector with indices characterized by desirable properties and sound methodological foundations to assess the systemic risk and network effects of the CC sector. First steps in this direction have been done by Ren et al (2020) and Guo et al (2021).…”

When Crypto Economics Meet Graph Analytics and Learning

“…The primary problem addressed in this paper is the gap in understanding and effectively leveraging the interplay between crypto economics and graph learning and analytics across the entire crypto world. There is currently a lack of comprehensive understanding of crypto economics, coupled with limited development in graph analytics and learning in the crypto market, which primarily focuses on major cryptocurrencies [11,12]. We detail the challenge of uncovering this interplay in two main aspects:…”

“…Network effects: The network effect, illustrating how a product's value grows with its user base, has been analyzed in crypto economics using graph analytics, focusing mainly on major cryptocurrencies. Ren et al [12] investigated the 30 largest cryptocurrencies from 2019 to 2020 by creating a network of cryptocurrencies linked by regression coefficients of returns, finding significant intercryptocurrency influence, particularly during the COVID-19 crisis.…”

“…The landscape of graph analytics and learning in crypto markets encompasses a wide range of research areas, such as transaction network analysis, fraud detection, market behavior, decentralization, and tokenomics. Studies examine the inter-correlation and influence of major cryptocurrencies [12]. Research also delves into decentralization, evaluating blockchain network topologies for robustness and efficiency [4], and investigates tokenomics by examining token distribution and incentives [3].…”

A Financial Risk Meter for China

“…The evolution of averaged λ j in a window of length k represents the variation of the systemic tail risks (Härdle et al (2016), Mihoci et al (2020), Ren et al (2020)), thus the FRM index measures joint tail events. Various markets' FRM indices are reported on http://frm.wiwi.hu-berlin.de.…”