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- W4280497097 abstract "Abstract In this study, a multivariate ARMA–GARCH model with fractional generalized hyperbolic innovations exhibiting fat-tail, volatility clustering, and long-range dependence properties is introduced. To define the fractional generalized hyperbolic process, the non-fractional variant is derived by subordinating time-changed Brownian motion to the generalized inverse Gaussian process, and thereafter, the fractional generalized hyperbolic process is obtained using the Volterra kernel. Based on the ARMA–GARCH model with standard normal innovations, the parameters are estimated for the high-frequency returns of six U.S. stocks. Subsequently, the residuals extracted from the estimated ARMA–GARCH parameters are fitted to the fractional and non-fractional generalized hyperbolic processes. The results show that the fractional generalized hyperbolic process performs better in describing the behavior of the residual process of high-frequency returns than the non-fractional processes considered in this study." @default.
- W4280497097 created "2022-05-22" @default.
- W4280497097 creator A5090460863 @default.
- W4280497097 date "2022-05-15" @default.
- W4280497097 modified "2023-09-27" @default.
- W4280497097 title "ARMA–GARCH model with fractional generalized hyperbolic innovations" @default.
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- W4280497097 doi "https://doi.org/10.1186/s40854-022-00349-2" @default.
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