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

Rui Ren, Michael Althof () and Wolfgang Härdle

No 2020-028, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: Cryptocurrencies; Network Dynamics; Portfolio Optimization; Quantile Regression; Systemic Risk; Financial Risk Meter (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cwa, nep-pay and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/230834/1/irtg1792dp2020-028.pdf (application/pdf)

Related works:
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:zbw:irtgdp:2020028

Access Statistics for this paper

More papers in IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series" Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().