Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model
Yanlin Shi () and
Yang Yang ()
Additional contact information
Yanlin Shi: Department of Actuarial Studies and Business Analytics, Macquarie University, North Ryde 2109, Australia
Yang Yang: Research School of Finance, Actuarial Studies and Statistics, The Australian National University, Acton 2601, Australia
Risks, 2018, vol. 6, issue 2, 1-28
In this paper, we propose an Adaptive Hyperbolic EGARCH (A-HYEGARCH) model to estimate the long memory of high frequency time series with potential structural breaks. Based on the original HYGARCH model, we use the logarithm transformation to ensure the positivity of conditional variance. The structural change is further allowed via a flexible time-dependent intercept in the conditional variance equation. To demonstrate its effectiveness, we perform a range of Monte Carlo studies considering various data generating processes with and without structural changes. Empirical testing of the A-HYEGARCH model is also conducted using high frequency returns of S&P 500, FTSE 100, ASX 200 and Nikkei 225. Our simulation and empirical evidence demonstrate that the proposed A-HYEGARCH model outperforms various competing specifications and can effectively control for structural breaks. Therefore, our model may provide more reliable estimates of long memory and could be a widely useful tool for modelling financial volatility in other contexts.
Keywords: long memory; structural change; GARCH; A-HYEGARCH (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:6:y:2018:i:2:p:26-:d:138135
Access Statistics for this article
Risks is currently edited by Prof. Dr. J. David Cummins
More articles in Risks from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().