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A novel time-varying FIGARCH model for improving volatility predictions.
- Source :
-
Physica A . Mar2022, Vol. 589, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- The FIGARCH model has received wide attention due to its ability to capture the features of volatility long-memory persistence and clustering. The classical FIGARCH model is based on the difference scheme of Grünwald–Letnikov fractional operators. This paper introduces the new class of FIGARCH processes for improving time-varying volatility predictions. Firstly, a novel FIGARCH model based on the Caputo fractional operators (FIGARCH-C model for short) is proposed. Secondly, a quasi-maximum likelihood estimation (QMLE) is used to estimate the parameters of the FIGARCH-C(1, d, 1), the FIGARCH(1, d, 1) and GARCH(1, 1) models. Finally, we apply the three models to Brent crude oil and S&P 500 returns and provide the comparison results of the three models. The results show that the FIGARCH and FIGARCH-C models outperformed the GARCH model in capturing the long memory in volatility. It is also found that the FIGARCH-C model is more sensitive to capture the change in the volatile period. • A novel FIGARCH model based on the Caputo fractional operators is proposed. • A quasi-maximum likelihood estimation was used to estimate the parameters. • The FIGARCH-C model behaves most accurately in the estimation and forecasting. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03784371
- Volume :
- 589
- Database :
- Academic Search Index
- Journal :
- Physica A
- Publication Type :
- Academic Journal
- Accession number :
- 154659158
- Full Text :
- https://doi.org/10.1016/j.physa.2021.126635