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Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data

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Abstract

We extend the literature on the effect of rare disaster risks on commodities by examining the effect of the El Niño-Southern Oscillation (ENSO) on crude oil via the recently developed kth order nonparametric causality-in-quantile framework, utilizing a long-range historical data set spanning the period 1876:01 to 2021:04. The methodology allows us to test for the predictive role of ENSO over the entire conditional distribution of not only real oil returns but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and regime changes. Empirical findings show that the Southern Oscillation Index (SOI), measuring the ENSO cycle, not only predicts real West Texas Intermediate (WTI) oil returns of the United States (US), but also volatility, over the entirety of the respective conditional distributions. The findings highlight the role of rare disaster risks over not only financial markets, but also commodities with significant implications for policymakers and investors.

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Notes

  1. The log-returns ensure that the oil data is mean-reverting, while the SOI is stationary in levels, which in turn meets the data requirements of the test employed. Understandably, we need to work with returns to analyze the impact on squared returns, i.e., volatility.

  2. Our description of the technical details of the quantile-based test is relatively compact and draws heavily on the expositions in Balcilar et al. (2020a, b), which dealt with the housing market. Additional applications involving financial and commodity markets can be found in the works of Balcilar et al. (2016a, b, 2018a, b, c), Bonaccolto et al. (2018), among others.

  3. An anonymous referee suggested that we distinguish between the effects of El Niño and La Niña episodes of the ENSO cycle. In this regard, following Cashin et al. (2017), we define SOI “anomalies” in our empirical model, calculated as a deviation of the SOI in any given month from its historical average, normalized (divided) by its historical standard deviation. Given this, sustained negative SOI anomaly values below − 1 (above + 1) indicate El Niño (La Niña) episodes. To capture the two phases of the ENSO cycle, we create a dummy variable which takes the value of one when SOI anomalies are negative (positive) and zero otherwise, and multiply the dummy variable by the SOI anomalies to obtain a metric for the El Niño (La Niña) episodes. When we re-conduct the causality-in-quantile test using El Niño or La Niña phases on real oil returns and volatility, our results, as reported in Table 5 in the Appendix of the paper, are qualitatively similar to those derived from the overall ENSO cycle. In other words, just like the ENSO cycle, El Niño and La Niña phases have the strongest predictability at the conditional median of real oil returns, and at the lowest conditional quantile of real oil returns volatility. Interestingly, when we compare across these two phases, in general, La Niña phases have relatively stronger predictive ability than El Niño events for both real oil returns and volatility. This is possibly because La Niña events lead to excessive cooling of temperatures, which in turn causes increases in demand for (heating) oil, and hence stronger effects on oil returns and volatility (Balcilar et al. 2021).

  4. The speculative ratio is measured as the trading volume divided by open interest for WTI futures traded on NYMEX (data obtained from Commodity Systems Inc.). Note that due to the availability of futures market data, the monthly speculative ratio series begins in 1983.

  5. Cakan et al. (2019) also establish a link between speculative behavior and herding in the global oil market associating greater speculation with herding behavior in major energy importer and exporter nations.

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Acknowledgements

We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.

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Authors and Affiliations

  1. Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL, 62026-1102, USA

    Riza Demirer

  2. Department of Economics, University of Pretoria, Pretoria, 0002, South Africa

    Rangan Gupta & Jacobus Nel

  3. Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008, Hamburg, Germany

    Christian Pierdzioch

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  1. Riza Demirer
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All authors contributed equally to the study concept, design, analysis, and final write up. All authors read and approved the final manuscript.

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Correspondence to Jacobus Nel.

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Appendix

Appendix

Table 5. kth order causality-in-quantile test results
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Demirer, R., Gupta, R., Nel, J. et al. Effect of rare disaster risks on crude oil: evidence from El Niño from over 145 years of data. Theor Appl Climatol 147, 691–699 (2022). https://doi.org/10.1007/s00704-021-03856-x

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