Modelling volatility of cryptocurrencies using Markov-Switching GARCH models
Guglielmo Maria Caporale and
Research in International Business and Finance, 2019, vol. 48, issue C, 143-155
This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1000 GARCH models are fitted to the log returns of the exchange rates of each of these cryptocurrencies to estimate a one-step ahead prediction of Value-at-Risk (VaR) and Expected Shortfall (ES) on a rolling window basis. The best model or superior set of models is then chosen by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH models may yield incorrect VaR and ES predictions, and hence result in ineffective risk-management, portfolio optimisation, pricing of derivative securities etc. These could be improved by using instead the model specifications allowing for asymmetries and regime switching suggested by our analysis, from which both investors and regulators can benefit.
Keywords: Cryptocurrencies; Volatility; Markov-switching; GARCH (search for similar items in EconPapers)
JEL-codes: C22 G12 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Modelling Volatility of Cryptocurrencies Using Markov-Switching Garch Models (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:48:y:2019:i:c:p:143-155
Access Statistics for this article
Research in International Business and Finance is currently edited by T. Lagoarde Segot
More articles in Research in International Business and Finance from Elsevier
Bibliographic data for series maintained by Haili He ().