Modelling Volatility Of Cryptocurrencies Using Markov-switching T Garch Models

Modelling volatility of cryptocurrencies using markov-switching t garch models

Modelling Volatility of Cryptocurrencies Using Markov-Switching GARCH Models

26 PagesPosted: 27 Sep 2018

Date Written: August 02, 2018

Abstract

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.

9. Volatility Modeling

More than 1,000 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.

Modelling volatility of cryptocurrencies using markov-switching t garch models

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.

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Keywords: cryptocurrencies, volatility, Markov-switching, GARCH

JEL Classification: C220, G120

Suggested Citation:Suggested Citation

Caporale, Guglielmo Maria and Zekokh, Timur, Modelling Volatility of Cryptocurrencies Using Markov-Switching GARCH Models (August 02, 2018).

CESifo Working Paper Series No.

Modelling volatility of cryptocurrencies using markov-switching t garch models

7167. Available at SSRN: https://ssrn.com/abstract=3251701

Modelling volatility of cryptocurrencies using markov-switching t garch models