Identifying Safe Haven Assets: Evidence from Fractal Market Hypothesis

P. S. Niveditha ()
Additional contact information
P. S. Niveditha: Affiliated to Cochin University of Science and Technology (CUSAT)

Computational Economics, 2025, vol. 65, issue 1, No 11, 313-335

Abstract: Abstract In this study, an attempt is made to identify commonly considered safe-haven assets through the Fractal Market Hypothesis (FMH) which assumes that the market is made up of heterogeneous agents and that there will be a dominance of the short-term investment horizon over the long-term investment horizon during a crisis period. The study considers the COVID-19 crisis as a critical period and examines the fractal behavior of the most commonly considered safe-haven assets (Gold, Bitcoin, Swiss franc and US dollar) for the sample period of 2015–2022 by using Continuous Wavelet Transform (CWT) methodology. The findings indicate that potential safe-haven assets can be identified through FMH. It also examines the potential safe-haven asset’s properties in more detail by employing a new methodology; the Wavelet Quantile Correlation (WQC) method. The study found that Gold is a safe haven only for the short run, whereas Bitcoin is a good diversifier. From the currencies, the Swiss franc is also not a good safe haven both in the short run and long run, and the US dollar is a safe haven for the long run.

Keywords: Fractal market hypothesis; Safe-haven assets; Wavelets; COVID-19 crisis (search for similar items in EconPapers)
JEL-codes: G10 G14 G15 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10572-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:65:y:2025:i:1:d:10.1007_s10614-024-10572-x

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10572-x

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().