Investigating the impact of geopolitical risks on the commodity futures

Abstract

This paper examines the effect of real-time global geopolitical risks (GPRs), acts (GPAs), and threats (GPTs) indices on monthly returns and volatility of several American commodity futures. By modeling volatility via an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), we provide evidence that GPRs and GPTs do not only impact but trigger adverse effects on the returns of crude oil, gold, platinum, and silver, while GPAs negatively affect the returns of crude oil, heating oil, platinum, and sugar futures. Furthermore, GPTs have a weak positive effect on corn futures volatility. Overall, our findings provide portfolio diversification benefits by showing how the impact of global GPRs, GPAs and GPTs on portfolio returns could be mitigated.

Keywords:

• Geopolitical risks
• geopolitical acts
• geopolitical threats
• commodity futures contracts
• returns and volatility
• EGARCH

PUBLIC INTEREST STATEMENT

Commodity futures contract is an agreement to purchase or sell a predetermined amount of the underlying commodity on a specific price and date in the future. These contracts are significant because they could be used to hedge or speculate an investment position. Some known factors that affect their prices are the weather, and macroeconomic fundamentals. In addition, the geopolitical risks seem to have an impact on their prices. This study examined the impact of geopolitical risks, acts and threats on the commodity futures monthly returns and volatility in the United States of America market. Therefore, we detected that the geopolitical risks and threats negatively affect the returns of the crude oil, gold, platinum, and silver, whereas the geopolitical acts have the same effect on of crude oil, heating oil, platinum, and sugar futures. Lastly, our results determined a weak positive relationship between the geopolitical threats and the corn futures volatility.

Disclosure statement

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

Notes

1. The BRICS association contains countries from the continents of Brazil, Russia, India, China, and South Africa.

2. Antonakakis et al. (2017) investigate the impact of the GPR index on the oil and stock markets. Using a VAR-BEKK-GARCH model and monthly data of the West Texas Intermediate oil and the Standard & Poor’s (S&P) 500 index from 1899 to 2016, and find that the GPR index has a negative impact primarily on oil returns and volatility, and to a smaller degree on the covariance between oil and stock markets. Plakandaras et al. (2019) using monthly data from January 1985 to June 2017 of the GPR index, WTI crude oil prices, and TVP-VAR models with various Bayesian methods, detect an adverse relationship between geopolitical risks and oil returns in the United States of America.

3. For the details of the 10-Year Treasury constant maturity rate and the Trade Weighted United States dollar-major currencies index, visit the websites of the Federal Reserve Bank of St. Louis (https://fred.stlouisfed.org/series/GS10) and Quandl.com (https://www.quandl.com/data/FRED/DTWEXM-Trade-Weighted-U-S-Dollar-Index-Major-Currencies), respectively.

4. Apart from a GARCH(1,1), we apply various types of GARCH, including GJR-GARCH(1,1) and EGARCH(1,1) (the results are available at the Appendix and upon request).

5. We have applied various robustness related tests, including normality tests, unit root tests, serial correlation tests, whether the conditional heteroscedasticity exists, etc. The most important ones are available and briefly discussed on part 5. The rest are available upon request. Still, it has to be noted here that the presence of the ARCH effects ratify the appropriateness of the EGARCH model for the selected data, which is the best fitted model compared to GARCH and GJR-GARCH. Also, on the variance equations the ARCH and GARCH effects are statistically significant, bearing signs that are consistent with the GARCH constraints. To determine whether there is an absence of serial correlation in the commodity futures returns series (R_t), we use the correlogram of standardised residuals squared tests. All models are free from serial correlation. Lastly, for validity purposes, we apply ARCH tests and detect the presence of homoscedasticity in the EGARCH models residuals.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Sokratis Mitsas

Dr. Petros Golitsis (PhD) is an Assistant Professor in Economics at the City College, University of York Europe Campus, a South-East European Research Centre Research Associate. His research interest covers monetary transmission channels, optimum currency areas, exchange rates and regimes, foreign exchange reserves, and the impact of geopolitical uncertainty on various macroeconomic and financial variables.

Sokratis Mitsas is an Analyst in the Risk Management, Credit Risk, Model Ownership & Data Management Department at ABN AMRO Bank N.V. He received his BA in Business Studies, in specialisation in Accounting and Finance, and MSc in Finance and Banking from the University of Sheffield. His research interest covers the impact of geopolitical uncertainty on various macroeconomic and financial variables.

Dr. Khurshid Khudoykulov is (DSc) is a Professor at department of finance from the Tashkent State University of Economics (TSUE), Tashkent. His research interests focus on asset pricing, capital markets, investment, and the impact of geopolitical uncertainty on various macroeconomic and financial variables.