ARMA–GARCH model with fractional generalized hyperbolic innovations
Sung Ik Kim ()
Additional contact information
Sung Ik Kim: Louisiana State University Shreveport
Financial Innovation, 2022, vol. 8, issue 1, 1-25
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.
Keywords: Generalized hyperbolic process; Time-changed Brownian motion; Long-range dependence; Fractional Brownian motion; ARMA–GARCH model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1186/s40854-022-00349-2 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00349-2
Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589
DOI: 10.1186/s40854-022-00349-2
Access Statistics for this article
Financial Innovation is currently edited by J. Leon Zhao and Zongyi
More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().