Cryptocurrenciesvalue-at-riskand expected shortfall: Do regime-switching volatility models improve forecasting?

【Author】 Maciel, Leandro

【Source】INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS

【影响因子】1.634

【Abstract】This paper evaluates the presence of regime changes in the log-returns volatility dynamics of cryptocurrencies using Markov-Switching GARCH (MS-GARCH) models. The empirical study compares the prediction performance of MS-GARCH against traditional single-regime GARCH methods for one-, five- and ten-steps-ahead volatility forecasting of six leading digital coins such as Bitcoin, Dashcoin, Ethereum, Litecoin, Monero and Ripple. Using a Bayesian approach, different MS-GARCH structures are estimated considering specifications up to three regimes, three scedastic functions and six error distributions, resulting in a total of 54 models for each cryptocurrency. Forecasts are compared according to an economic criterion, that is, through the estimation of Value-at-Risk (VaR) and Expected Shortfall (ES) risk measures. The results support the evidence of regime changes in the volatility process of selected cryptocurrencies and show that MS-GARCH models do provide more accurate VaR and ES forecasts than their single-regime counterparts.

【Keywords】cryptocurrencies; forecasting; risk management; MS-GARCH; regime switching

【发表时间】2020 JUL

【收录时间】2022-01-02

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