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Since its inception in 2009, cryptocurrency's acceptance remains controversial because of its nature, absence of inherent worth, and ambiguous issuance authority. This resulted in the significant volatility and unpredictability of the cryptocurrency price, leading to the loss of investors' funds. In this study, we model the heteroskedastic volatility of cryptocurrencies and forecast the future price using GARCH. Historical analysis of cryptocurrencies shows volatility ranging from medium to extremely high volatility. Like other financial assets, cryptos exhibit volatility clustering, i.e., high volatility leading to further high volatility and low volatility leading to low volatility forming volatility clusters. In this paper, we apply seven variants, namely Standard GARCH (SGARCH), IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), Asymmetric Power ARCH (APARCH) (1,1), Threshold GARCH (TGARCH) (1,1), and Component GARCH (CGARCH) (1,1), of the GARCH model to forecast the volatility of cryptocurrencies and identify the bestfit model based on forecasting accuracy. We find that among the seven variants of GARCH, EGARCH and CGARCH models appropriately forecast the volatility of cryptocurrenc

Author

B. N. S. S. Kiranmai; Viswanathan Thangaraj

Publisher

SPRINGER VERLAG, SINGAPOR

Published

2023

Language

EN

ISBN

9789811993794

Subjects

Computer Science, Engineering, Science