Risk analysis of high frequency precious metals returns by using long memory model

Muhammad Naeem, Muhammad Shahbaz, Kashif Saleem and Faisal Mustafa

Resources Policy, 2019, vol. 61, issue C, 399-409

Abstract: This paper examines the long memory feature in the conditional volatility and Value at Risk calculations of precious metals returns at different time frequencies. In doing so, we employ high frequency data while using FIGARCH models. Furthermore, significant long memory characteristics have been detected in absolute returns by using semiparametric Local Whittle (LW) approximation. The empirical evidence of the local memory parameter across different time intervals gives consistent approximates of the long memory parameter which indicates that the series show some degree of self-similarity. Further, empirical results show that the long memory characteristics remains fully consistent across different time intervals of data for conditional and unconditional volatility measures. This study is useful for investors and traders (with different trading horizons) with regard to forecasting volatility and calculating or implementing trading strategies at different time frequencies.

Keywords: Long memory property; Volatility; Precious metals; FIGARCH (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420717303744
Full text for ScienceDirect subscribers only

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:eee:jrpoli:v:61:y:2019:i:c:p:399-409

DOI: 10.1016/j.resourpol.2018.01.013

Access Statistics for this article

Resources Policy is currently edited by R. G. Eggert

More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().