Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests

Aviral Tiwari and Claudiu Albulescu ()

Applied Energy, 2016, vol. 179, issue C, 272-283

Abstract: We use a continuous wavelet approach and deploy asymmetric, multi-horizon Granger-causality tests between the return series of the oil price and the India-US exchange rate, over the time-span 1980M1–2016M2. The results highlight important co-movements in the post-reform period, especially for the 2–4-years band. The wavelet Granger-causality tests show that the exchange rate Granger-causes the oil price in the long run, while the opposite applies in the short run. Moreover, we find that the Granger-causal relationship between variables is non-linear, asymmetric and indirect, which will help policymakers and traders to make better strategic and investment decisions.

Keywords: Cyclical and anti-cyclical effects; Wavelet coherence; Oil price-exchange rate; Granger causality; India (search for similar items in EconPapers)
JEL-codes: C40 E32 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261916309278
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:appene:v:179:y:2016:i:c:p:272-283

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2016.06.139

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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