Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect
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 (GA...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/19176/ http://journalarticle.ukm.my/19176/1/25.pdf |
| _version_ | 1848814769489838080 |
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| author | Zahid, Mamoona Iqbal, Farhat Raziq, Abdul Sheikh, Naveed |
| author_facet | Zahid, Mamoona Iqbal, Farhat Raziq, Abdul Sheikh, Naveed |
| author_sort | Zahid, Mamoona |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | 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 (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. |
| first_indexed | 2025-11-15T00:39:21Z |
| format | Article |
| id | oai:generic.eprints.org:19176 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:39:21Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:191762022-08-01T04:33:13Z http://journalarticle.ukm.my/19176/ Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect Zahid, Mamoona Iqbal, Farhat Raziq, Abdul Sheikh, Naveed 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 (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. Penerbit Universiti Kebangsaan Malaysia 2022-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19176/1/25.pdf Zahid, Mamoona and Iqbal, Farhat and Raziq, Abdul and Sheikh, Naveed (2022) Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect. Sains Malaysiana, 51 (3). pp. 929-942. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil3_2022/KandunganJilid51Bil3_2022.html |
| spellingShingle | Zahid, Mamoona Iqbal, Farhat Raziq, Abdul Sheikh, Naveed Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title | Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title_full | Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title_fullStr | Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title_full_unstemmed | Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title_short | Modeling and forecasting the realized volatility of bitcoin using realized HAR-GARCH-type models with jumps and inverse leverage effect |
| title_sort | modeling and forecasting the realized volatility of bitcoin using realized har-garch-type models with jumps and inverse leverage effect |
| url | http://journalarticle.ukm.my/19176/ http://journalarticle.ukm.my/19176/ http://journalarticle.ukm.my/19176/1/25.pdf |