Articlehybrid2018

Modelling Volatility of Cryptocurrencies Using Markov-Switching GARCH Models

199 citations0/0 files storedResearch in International Business and Finance

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Financial Risk and Volatility Modeling Market Dynamics and Volatility Stock Market Forecasting Methods

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. 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. 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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References

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Cited by

2021Risk Transmissions between Bitcoin and Traditional Financial Assets during the COVID-19 Era: The Role of Global Uncertainties 2021Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey 2021On the stability of stablecoins 2021Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures 2019Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models 2020

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