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Applications in Energy Finance pp 115–143Cite as

Palgrave Macmillan

Tracing the Sources of Contagion in the Oil-Finance Nexus

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Abstract

We introduce an approach to trace the genesis of contagion occurring in the oil-finance nexus, which consolidates veteran non-linear oil price measures derived from the empirical oil literature, to construct a rule-based specification for filtering structural oil market shocks into calm and extreme episodes. Such identified conditions are useful to understand how changing scenarios in the international crude oil market influence the dynamic relationships between the crude oil, exchange rate, and stock markets. As we are the first to explicitly consider how the relationship between the exchange rate and stock market change under extreme oil market shocks, our applications to a small emerging oil-exporter provide novel results about this particular linkage. We find that the positive supply shocks and negative demand shocks associated with the 2014/2015 oil price crash coincide with a marked increase in the inverse exchange rate-stock market relationship. This highlights the importance of including exchange rates when analysing the dependence between oil and stock markets. Our results also show that international financial crises, such as the Asian flu and dot-com crash, are episodes of contagion in an otherwise weak oil-stock market relationship. In addition, we provide findings which are consistent with previous empirical literature that extreme demand-side oil market shocks tend to dominate the absolute increase in cross-market linkages and that the 2008/2009 global financial crisis is the most prominent contemporary event in the oil-finance nexus in a pre-COVID-19 world.

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Notes

  1. 1.

    A switch to a dirty floating exchange rate from a fixed exchange rate regime in Trinidad and Tobago occurred in April 1993. On this grounds we start our analysis in January 1996, to allow for some time for the economy to acclimatise to the new exchange rate regime.

  2. 2.

    The data are available from the US Energy Information Administration at www.eia.gov/international/data/world and accessed in November 2018.

  3. 3.

    It is important to note that Hamilton (2018) points out a data transformation error in the index of nominal freight rates underlying the Kilian (2009) global real economic activity measure, where the log operator is performed twice. Kilian (2019) acknowledges this coding error and corrects the global business cycle index. We use this updated data, which are available at https://sites.google.com/site/lkilian2019/research/data-sets and accessed in November 2018.

  4. 4.

    These data are available from the Federal Reserve Economic Data (FRED) at fred.stlouisfed.org/, accessed in November 2018. Like Broadstock and Filis (2014), we use the Brent benchmark instead of the West Texas Intermediate (WTI) to represent the global price of oil. The latter has been traded at a discounted price since 2011 due to the US shale boom (Kilian, 2016). In light of such developments, Brent oil has further fortified its prominence as global benchmark, while the WTI price increasingly reflects US-specific dynamics (Manescu & Van Robays, 2016). Moreover, Trinidad and Tobago produces water-borne crude which is pegged to the Brent crude oil price benchmark, trading at either a premium or a discount to this international reference price.

  5. 5.

    Data are sourced from the International Monetary Fund (IMF) International Financial Statistics and retrieved via Thomson Reuters Eikon, accessed in November 2018.

  6. 6.

    These data are calculated using data from the Central Bank of Trinidad and Tobago (CBTT), and are available from www.central-bank.org.tt/statistics/data-centre and accessed in November 2018.

  7. 7.

    Returns are calculated as the first difference in the natural logarithm for each series, times 100.

  8. 8.

    We find no statistically significant asymmetric responses to positive and negative news for exchange rates and stock returns. However, in the case of oil returns, the asymmetric volatility tests show that the individual sign bias tests convey no asymmetric volatility in the standardised residuals, but the joint effects test is statistically significant. Therefore, we consider asymmetric GARCH variants for this particular series to accommodate for this artefact. Yet, an EGARCH(1,1) for oil returns, which we find to be the most suitable alternative GARCH specification for this series, shows that the leverage effects term is not significant. Further, the differences in dynamic correlations estimated from a model where oil returns follows either a GARCH(1,1) or an EGARCH(1,1) specification is negligible. As such, we revert to the parsimonious GARCH(1,1) model for oil returns.

  9. 9.

    The National Bureau of Economic Research defines the timespan of the Great Recession in the US from December 2007 to June 2009. The dating is obtained from www.nber.org/cycles, and accessed in November 2018.

  10. 10.

    The DCC model coefficients and dynamic correlations are estimated with the rmgarch package in R (see Ghalanos, 2019).

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Acknowledgements

Earlier versions of this chapter were presented at the 1st International Conference on Energy, Finance, and the Macroeconomy in November 2017 (Montpellier Business School, France); at the Keele Business School Economics and Finance Research Group Seminar Series in February 2019 (Keele University, UK); at the Money, Macro, and Finance Ph.D. Conference in April 2019 (City, University of London, UK); and at the INFINITI 2019 Conference on International Finance in June 2019 (Adam Smith Business School, University of Glasgow, UK). We are grateful to the participants and assigned discussants at these events for their insightful feedback, which have served to refine the research. The usual disclaimer applies. We also declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Portsmouth Business School, University of Portsmouth, Portsmouth, UK

    Scott M. R. Mahadeo

  2. Bristol Business School, University of the West of England, Bristol, UK

    Reinhold Heinlein

  3. Keele Business School, University of Keele, Keele, UK

    Gabriella D. Legrenzi

  4. CESifo Research Network, Munich, Germany

    Gabriella D. Legrenzi

  5. Rimini Centre for Economic Analysis, Rimini, Italy

    Gabriella D. Legrenzi

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  1. Scott M. R. Mahadeo
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Correspondence to Scott M. R. Mahadeo .

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Editors and Affiliations

  1. Department of Accounting and Finance, Hellenic Mediterranean University, Heraklion, Crete, Greece

    Christos Floros

  2. Department of Accounting and Finance, Hellenic Mediterranean University, Heraklion, Crete, Greece

    Ioannis Chatziantoniou

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Mahadeo, S.M.R., Heinlein, R., Legrenzi, G.D. (2022). Tracing the Sources of Contagion in the Oil-Finance Nexus. In: Floros, C., Chatziantoniou, I. (eds) Applications in Energy Finance. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-92957-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-92957-2_5

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