Petroleum prices and equity sector returns in petroleum exporting and importing countries: an analysis of volatility transmissions and hedging

ABSTRACT

This paper examines the direction and magnitude of volatility transmissions between prices of petroleum and stock sector indices of the net petroleum exporter, Mexico, and the net petroleum importer, the United Kingdom. The sector indices are self-constructed utilizing daily data of 258 unique stocks listed in eight sectors from January 2005 to September 2018 that permits implementing the same methodological framework across two markets. The study applies the VAR-GARCH model that enables to study bidirectional spillover effects. The results provide evidence of volatility spillovers between petroleum prices and sector indices. The effects are more apparent in the case of the net exporter, where the bidirectional volatility transmissions were observed. The computed optimal portfolio weights and hedge ratios considerably vary among sectors of both countries. The findings emphasize the crucial role of comprehending the heterogeneity of sectors for the management of investment portfolios.

KEYWORDS:

• Petroleum prices
• stock sector returns
• volatility transmission
• hedge ratios

JEL CLASSIFICATION:

• G11
• G12
• Q43

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ For details see: Mexicanist, https://www.mexicanist.com/l/mexican-stock-exchange-b-m-v/

² ONS, Ownership of UK quoted shares: 2018.

³ Refer to Appendix for details.

⁴ Although, Akaike Information Criterion, Schwarz Bayesian Criterion and Hannan-Quinn chose different lags, for the sake of parsimony, the model with one lag was selected, as based on the preliminary analysis, the model with longer lags showed negligible differences.

⁵ Based on the Schwarz Bayesian Criterion, it was found that in all cases the GARCH(1,1) process fits better.

⁶ BP Statistical Review of World Energy 2019.

⁷ BP Statistical Review of World Energy 2019.

⁸ BP Statistical Review of World Energy 2019.

⁹ Results available upon request.

¹⁰ In addition, the proposed in this study approach for the manual construction of sector indices has a statistical advantage. The two-sample Kolmogorov-Smirnov test was applied to check for differences in distributions of each aggregate market and sector index pairs. The null hypothesis that two datasets are from the same distribution was rejected for two sector indices of Mexico and five sector indices of the United Kingdom. Results available upon request.

¹¹ The daily returns for each of data series are computed as $100\times ln\left(P_t/P_{t-1}\right)$.

¹² Annualised mean returns for Brent, S&P BMV IPC and FTSE 100 indices are 5.35%, 6.15% and 0.48%, respectively.

¹³ Applied as a preliminary test for presence of ARCH effects in residuals. The test statistics at orders from one to four are significant at 1% level too.

¹⁴ The series are also stationary when the test was performed using Akaike Information Criterion for selection of lags.