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Uncertainty, Expectations and Asset Price Dynamics pp 31–50Cite as

Oil Market Volatility: Is Macroeconomic Uncertainty Systematically Transmitted to Oil Prices?

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Part of the book series: Dynamic Modeling and Econometrics in Economics and Finance ((DMEF,volume 24))

Abstract

The aim of this contribution is to analyze the impact of macroeconomic uncertainty on the oil market. We rely on a robust measure of macroeconomic uncertainty based on a wide range of monthly macroeconomic and financial indicators, which is linked to predictability rather than to volatility. We estimate a structural threshold vector autoregressive (TVAR) model to account for the varying effect of macroeconomic uncertainty on oil price returns depending on the degree of uncertainty, from which we derive a robust proxy of oil market uncertainty. Our findings show that a significant component of oil price uncertainty can be explained by macroeconomic uncertainty. In addition, we find that the recent 2007–2009 recession has generated an unprecedented episode of high uncertainty in the oil market that is not necessarily accompanied by a subsequent volatility in the price of oil. This result highlights the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility.

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Notes

  1. 1.

    See Henry (1974), Bernanke (1983), Brennan and Schwartz (1985), Majd and Pindyck (1987), Brennan (1990), Gibson and Schwartz (1990), Bloom et al. (2007), Bloom (2009), Edelstein and Kilian (2009), and Bredin et al. (2011) among others.

  2. 2.

    Considering the uncertainty channel, the asymmetric responses of real output to oil price shocks may come from the fact that uncertainty tends to amplify the effect of unexpected oil price increases and offset the impact of unexpected oil price decreases (see Kilian, 2014). It is worth mentioning that Georges Prat is an internationally recognized specialist in the analysis of expectations. He has written several contributions on this topic, particularly on rational expectations (see Prat (1994, 1995) and Gardes and Prat (2000) among others). Among his numerous papers, his 2011’s article (see Prat and Uctum, 2011) deals with the modeling of expectations on the oil market.

  3. 3.

    It should be noticed that Bernanke (1983) and Pindyck (1991) consider the oil price as exogenous with respect to the US economy, which is not consistent with the recent literature about the endogenous component of oil prices (see Kilian, 2008a).

  4. 4.

    Other empirical papers exist in the literature, such as Favero et al. (1994), Lee et al. (1995), and Ferderer (1996), but they either treat oil as exogenous or use data from the pre-1973 period.

  5. 5.

    See references in Sect. 2.

  6. 6.

    This approach is therefore theoretically robust to the endogenous component of commodity prices, in line with the recent literature (see references in Sect. 2).

  7. 7.

    See Bloom (2009), Bloom et al. (2010, 2012), Gilchrist et al. (2010), Arellano et al. (2011), Bachmann and Bayer (2011), Baker et al. (2011), Basu and Bundick (2011), Knotek and Khan (2011), Fernández-Villaverde et al. (2011), Schaal (2012), Leduc and Liu (2012), Nakamura et al. (2012), Bachmann et al. (2013), and Orlik and Veldkamp (2013) among others.

  8. 8.

    See Jurado et al. (2015). The papers cited in Sect. 1 related to the study of the relationship between uncertainty and oil prices have used such indicators.

  9. 9.

    Recall that removing the forecastable component of y jt is crucial to avoid erroneously categorizing predictable variations as uncertain.

  10. 10.

    Dealing with monthly data and focusing on macroeconomic uncertainty, we consider in this work the “common macroeconomic uncertainty” measure.

  11. 11.

    Before this date, there was no global market for crude oil and the price of oil in the USA was regulated by the government.

  12. 12.

    One exception for the case of oil is the 1990s, where the flow supply shocks have played an important role (see Kilian and Murphy, 2014).

  13. 13.

    See Kilian (2014) for a review.

  14. 14.

    See Balke (2000) for a detailed presentation of TVAR processes as well as Tong (2010) and Hansen (2011) for general developments on threshold models. Van Robays (2013) uses the TVAR methodology to examine uncertainty on the oil market.

  15. 15.

    See the detailed results in Joëts et al. (2017).

  16. 16.

    See Chan and Jeliazkov (2009) and Chan and Hsiao (2013) for more details. The Matlab code used to estimate the moving average stochastic volatility model is freely available from the website of Joshua Chan. We obtain 20,000 draws from the posterior distribution using the Gibbs sampler after a burn-in period of 1000.

  17. 17.

    http://www.econ.nyu.edu/user/ludvigsons/. Since the submission of the present paper, an updated version of the database has been made available in February 2018 and can be downloaded at: https://www.sydneyludvigson.com/data-and-appendixes/.

  18. 18.

    We focus on short-run uncertainty (h = 1) because the effects have been largely documented both theoretically and empirically in the literature (see Bloom, 2014, for a review). For the sake of completeness, we have also estimated uncertainty at longer horizons, namely, 3 and 12 months. The corresponding figures are available upon request to the authors.

  19. 19.

    See Barsky and Kilian (2002, 2004), Kilian (2008a,b, 2009), Kilian and Murphy (2012, 2014), and Kilian and Hicks (2013) to name a few.

  20. 20.

    According to the US Energy Information Administration, the total Saudi Arabia crude oil production significantly decreases from 9550.136 thousand barrels per day in 2005 to 8721.5068 thousand barrels per day in 2007.

  21. 21.

    At the beginning of the 1980s, the strategy of Saudi Arabia to shut down production (compensating higher oil production elsewhere in the world) was initiated to prevent an oil price decline, without success. Saudi Arabia finally decided to ramp production back up in 1986, causing an oil shock from $27/barrel in 1985 to $12/barrel in 1986 (see Kilian and Murphy, 2014).

  22. 22.

    The lag order of the VAR specification is 3, as selected by usual information criteria.

  23. 23.

    See Kilian (2008a, 2009), and Kilian and Murphy (2012, 2014) among others.

  24. 24.

    See Kilian (2009), Baumeister and Peersman (2013), and Kilian and Murphy (2012).

  25. 25.

    We only report the results with Saudi Arabia crude oil production because they are more significant. Results from the global crude oil production are available upon request to the authors.

  26. 26.

    Similar to Kilian and Murphy (2014), we scaled the data on crude oil inventories by the ratio of OECD petroleum stocks over the US petroleum stocks for each time period.

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Acknowledgements

This contribution largely relies on Joëts, Mignon, and Razafindrabe (2017). We are grateful to Nathan Balke for providing us with his code for the TVAR estimations. We would like to thank Nick Bloom, Soojin Jo, and Lutz Kilian for their constructive comments and suggestions that helped us improve an earlier version of the work. The usual disclaimers apply.

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Authors and Affiliations

  1. Banque de France, International Macroeconomic Division and EconomiX-CNRS, University of Paris Nanterre, Nanterre, France

    Marc Joëts

  2. EconomiX-CNRS, University of Paris Nanterre and CEPII, Nanterre, France

    Valérie Mignon

  3. EconomiX-CNRS and CREM-CNRS, University of Rennes 1, Rennes, France

    Tovonony Razafindrabe

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  1. Marc Joëts
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  2. Valérie Mignon
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  1. Lille University, Lille Cedex, France

    Fredj Jawadi

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Joëts, M., Mignon, V., Razafindrabe, T. (2018). Oil Market Volatility: Is Macroeconomic Uncertainty Systematically Transmitted to Oil Prices?. In: Jawadi, F. (eds) Uncertainty, Expectations and Asset Price Dynamics. Dynamic Modeling and Econometrics in Economics and Finance, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-98714-9_2

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