Moments-based spillovers across gold and oil markets

Matteo Bonato, Rangan Gupta, Chi Keung Marco Lau, Shixuan Wang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we use intraday futures market data on gold and oil to compute returns, realized volatility, volatility jumps, realized skewness and realized kurtosis. Using these daily metrics associated with two markets over the period of December 2, 1997 to May 26, 2017, we conduct linear, nonparametric, and time-varying (rolling) tests of causality, with the latter two approaches motivated due to the existence of nonlinearity and structural breaks. While, there is hardly any evidence of spillovers between the returns of these two markets, strong evidence of bidirectional causality is detected for realized volatility, which seems to be resulting from volatility jumps. Evidence of spillovers are also detected for the crash risk variables, i.e., realized skewness, and for realized kurtosis as well, with the effect on the latter being relatively stronger. Based on a moments-based test of causality, evidence of co-volatility is deduced, whereby we find that extreme positive and negative returns of gold and oil tend to drive the volatilities in these markets. In our robustness check, we identify a causal chain in the realized volatility from oil to gold via the financial stress. Our results have important implications for not only investors, but also policymakers.

Original languageEnglish
Article number104799
JournalEnergy Economics
Volume89
DOIs
Publication statusPublished - 5 Jun 2020

Bibliographical note

Funding Information:
We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours. We also thank Professor Yi-Ting Chen for kindly providing us with the codes to estimate the Granger Causality in Moments. The research of Shixuan Wang was supported by the SPEIR Project Fund at the University of Reading.

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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