Modeling and Forecasting the Realized Volatility of Bitcoin using Realized HAR-GARCH-type Models with Jumps and Inverse Leverage Effect

نویسندگان

چکیده

Using the high-frequency data of Bitcoin, this study aims to model time-varying volatility identified in residuals heterogeneous autoregressive (HAR) realized using symmetric, asymmetric and long-memory generalized conditional heteroscedastic models (GARCH) models. We further extended these by incorporating jumps continuous components estimators investigating impact inverse leverage effect. The Diebold Mariano confidence set test confirm that forecasting performance HAR-type can be effectively improved innovations. long memory HAR-GARCH with provided better accuracy for Bitcoin as compared other findings may benefit individual investors risk managers who wish minimize risks diversify their portfolios maximize profits Bitcoin’s investment.

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ژورنال

عنوان ژورنال: Sains Malaysiana

سال: 2022

ISSN: ['0126-6039', '2735-0118']

DOI: https://doi.org/10.17576/jsm-2022-5103-25