Bitcoin returns and risk: A general GARCH and GAS analysis

Show simple item record Troster, Victor Tiwari, Aviral Kumar Shahbaz, Muhammad Macedo, Demian Nicolás 2020-03-17T11:37:20Z
dc.description.abstract [eng] This paper performs a general GARCH and GAS analysis for modelling and forecasting bitcoin returns and risk. Since Bitcoin trading exhibits excess volatility compared with other securities, it is important to model its risk and returns. We consider heavy-tailed GARCH models as well as GAS models based on the score function of the predictive conditional density of the bitcoin returns. We compare out-of-sample 1%-Value-at-Risk (VaR) forecasts under 45 different specifications using three backtesting procedures. We find that GAS models with heavy-tailed distributions provide the best out-of-sample forecast and goodness-of-fit properties to bitcoin returns and risk modelling. Normally-distributed GARCH models are always outperformed by heavy-tailed GARCH or GAS models. Besides, heavy-tailed GAS models provide the best conditional and unconditional coverage for 1%-VaR forecasts, illustrating the importance of modelling excess kurtosis for bitcoin returns. Hence, our findings have important implications for risk managers and investors for using bitcoin in optimal hedging or investment strategies.
dc.format application/pdf
dc.relation.ispartof Finance Research Letters, 2019, vol. 30, p. 187-193
dc.rights , 2019
dc.subject.classification 33 - Economia
dc.subject.classification 339 - Comerç. Relacions econòmiques internacionals. Economia mundial. Màrqueting
dc.subject.other 33 - Economics. Economic science
dc.subject.other 339 - Trade. Commerce. International economic relations. World economy
dc.title Bitcoin returns and risk: A general GARCH and GAS analysis
dc.type info:eu-repo/semantics/article 2020-03-17T11:37:20Z
dc.embargo 10000-01-01
dc.subject.keywords Bitcoin
dc.subject.keywords Cryptocurrency
dc.subject.keywords Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models
dc.subject.keywords Generalized Auto-regressive Score (GAS) models
dc.subject.keywords Forecast performance
dc.rights.accessRights info:eu-repo/semantics/closedAccess

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