Feedback spillover dynamics of crude oil and global assets indicators: A system-wide network perspective

Vipul Kumar Singh, Pawan Kumar and Shreyank Nishant

Energy Economics, 2019, vol. 80, issue C, 321-335

Abstract: Crude oil, the critical driver of the world economy, is quick to be blamed for causing volatility in all classes of assets, viz. equity, commodities, bonds, and currencies. The feedback mechanism between crude oil and these four asset classes makes the volatility spillover connectedness dynamics more complex and an important area to explore. Henceforth, we quantify and analyze the time-varying system-wide volatility spillover connectedness dynamics of crude oil and global asset indicators (GAIs) covering four major asset classes for the period from 2000 to 2016. Additionally, we have used the confluence of generalized error variance decomposition (GEVD) and network diagrams. According to the findings of this paper, crude oil is affected more by the GAIs than vice versa, thus making the crude oil price more vulnerable to volatility fluctuations in GAIs. On a pairwise basis, the study finds that commodities and currency are more tightly knit to crude oil, with CADUSD and gold being more sensitive to crude oil price fluctuations. In addition, the equity indices of the US and the UK have become more sensitive to crude oil price volatility following the GFC. The study opens future discourse for portfolio managers and policymakers to explore the spillover pattern and act accordingly by mitigating risk via the compensatory mechanism of positive/negative spillover pairs.

Keywords: Bonds; Commodity; Connectedness; Crude oil; Currency; Equity; Generalized error variance decomposition; Spillover; Volatility; Network (search for similar items in EconPapers)
JEL-codes: C5 G1 G11 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:80:y:2019:i:c:p:321-335

DOI: 10.1016/j.eneco.2019.01.005

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