Open AccessModeling and Forecasting the Realized Volatility of Bitcoin using Realized HAR-GARCH-type Models with Jumps and Inverse Leverage Effect
Author(s) -
Mamoona Zahid,
Farhat Iqbal,
Abdul Raziq,
Naveed Sheikh
Publication year - 2022
Publication title -
sains malaysiana
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 29
ISSN - 0126-6039
DOI - 10.17576/jsm-2022-5103-25
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , volatility (finance) , autoregressive model , leverage effect , forward volatility , estimator , realized variance , heteroscedasticity , economics , leverage (statistics) , stochastic volatility , computer science , mathematics , statistics
Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.