Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility

Rhenan G. S. Queiroz and Sergio A. David ()
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Rhenan G. S. Queiroz: Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
Sergio A. David: Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil

Risks, 2023, vol. 11, issue 12, 1-13

Abstract: Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.

Keywords: risk assets; Bitcoin; computer modeling; simulation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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