Sains Malaysiana 50(3)(2022): 929-942

http://doi.org/10.17576/jsm-2021-5103-25

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

(Memodel dan Meramalkan Kemeruapan Nyata Bitcoin menggunakan Model Nyata Jenis HAR-GARCH dengan Lompatan dan Kesan Tuasan Songsang)

MAMOONA ZAHID¹, FARHAT IQBAL¹*, ABDUL RAZIQ¹ & NAVEED SHEIKH²

¹Department of Statistics, University of Balochistan, Quetta, Pakistan

  ²Department of Mathematics, University of Balochistan, Quetta, Pakistan

Diserahkan: 14 Februari 2021/Diterima: 13 Ogos 2021

ABSTRACT

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.

Keywords: Bitcoin; HAR-GARCH; high-frequency data; inverse leverage; realized volatility

ABSTRAK

Dengan menggunakan data frekuensi tinggi Bitcoin, kajian ini bertujuan untuk memodelkan kemeruapan berbeza masa yang dikenal pasti dalam residu model autoregresi heterogen (HAR) daripada kemeruapan nyata menggunakan model simetri, asimetri dan memori panjang teritlak autoregresi bersyarat heteroskedastik (GARCH). Model-model ini terus diperluaskan dengan memasukkan lompatan dan komponen berterusan dalam penaksir kemeruapan nyata dan mengkaji kesan tuasan songsang. Diebold Mariano dan model ujian set keyakinan mengesahkan bahawa prestasi ramalan model jenis HAR dapat ditingkatkan dengan berkesan melalui inovasi ini. Model memori panjang HAR-GARCH dengan lompatan dan komponen berterusan memberikan ketepatan ramalan yang lebih baik untuk kemeruapan Bitcoin berbanding model kemeruapan nyata yang lain. Hasil kajian ini dapat memberi manfaat kepada pelabur individu dan pengurus risiko yang ingin meminimumkan risiko dan mempelbagaikan portfolio mereka untuk memaksimumkan keuntungan dalam pelaburan Bitcoin.

Kata kunci: Bitcoin; data frekuensi tinggi; HAR-GARCH; kemeruapan nyata; tuasan songsang

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*Pengarang untuk surat-menyurat; email: farhatiqb@gmail.com