Economic and geopolitical uncertainty vs energy variables: exploring connectedness in the LNG freight market

ABSTRACT

This paper contributes to the literature by applying quantile connectedness methodology to explore the impact of geopolitical and economic uncertainty on the volatility of LNG freight rates. Our findings justify modelling this market using econometric methodologies that depart from the classical Gaussian assumptions. First, we find asymmetric spillover effects between LNG freight rates, uncertainty, and energy variables, with the impact of uncertainty on the LNG rates becoming more intense at the tails of the distribution. Second, we find that the marginal variation in LNG freight rates after a shock is attributable to geopolitical and economic uncertainty, as the energy variables appear to absorb the marginal effects. Our results point to profound spillovers in the LNG freight market resulting from the pandemic, the war in Ukraine and government interventions to mitigate the adverse impact of these developments. These findings contribute to the LNG freight rate risk management. This article is a revised and expanded version of a paper entitled «Assessing the Impact of uncertainty and energy variables on LNG freight rates: A dynamic quantile connectedness analysis» presented at European Financial Management Association (EFMA) 2023 annual conference, Cardiff Business School, Cardiff University, on Thursday, June 29, 2023.

KEYWORDS:

• Oil prices
• LNG freight rates
• economic uncertainty
• geopolitical uncertainty
• quantile connectedness

Acknowledgments

The authors are grateful to Professor Samuel de Paiva Naves Mamede (Illinois State University and Mackenzie Presbyterian University) for helpful comments and suggestions during the EFMA 2023 Conference and to David Gabauer for providing the code in R for the connectedness analysis.

Disclosure statement

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

Author contributions

The authors have equally contributed to all parts of this paper. All the authors have read and approved the final manuscript.

Data availability statement

The data employed in this research paper and the codes to replicate the results are available upon request.

Consent for publication

This study presents original material that has not been published elsewhere.

Notes

1. In accordance with Directive 2014/94/EU of the European Parliament, the EU is to build a network of stations for refilling LNG in seaports by 2025 and on the mainland by 2030 that will include LNG terminals, tanks, mobile containers, bunker ships and barges (Gałczyński et al. 2017).

2. Basset and Koenker (1982), Koenker and Machado (1999), Koenker (2005), You et al. (2017), Shahzad, Shahbaz, Ferrer and Kumar (2017), Nusair and Olson (2019) use sample sizes similar to ours.

3. According to the National Oceanic and Atmospheric Administration (NOAA) «Temperature anomaly means a departure from a reference value or long-term average. A positive (negative) anomaly indicates that the observed temperature was warmer (cooler) than the reference value […] Temperature anomaly normalizes the data so they can be compared and combined to more accurately represent temperature patterns».

4. See Appendix A.1 for the full methodology of QC normality test.

5. We also apply conventional unit root test and unit root test that allows for structural breaks, as well as seasonality test. See Appendix A.2.

6. Throughout the text, the terms ‘conditional quantile’ and ‘quantile’ are used interchangeably, meaning conditional quantile.

7. We have performed various robustness tests using alternative window length and forecast horizon, which indicate similar qualitative findings. The results are available upon request.

8. We perform a robustness analysis according to Koenker and Basset’s (1978) quantile approach, (see Appendix B.1), which confirms the asymmetric nature of the relationship between the LNG freight rates, the uncertainty variables and the energy variables.