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Oil price shocks and global liquidity: macroeconomic effects on the Brazilian real

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Abstract

This paper examines the effects of the interaction between the oil market and measures of global liquidity on the Brazilian real against the US dollar, using an SVAR framework. The results show that approximately 15% of the variance of the real exchange rate is associated with oil-specific demand shocks in the long run. Supply and aggregate demand shocks are less important. The recovery of the Brazilian currency in the aftermath of the global financial crisis is more related to global liquidity than oil prices. Oil price changes affect the interest rate spread, which puts further pressure on the real exchange rate. Our results shed light on the impact of oil price shocks on the Brazilian economy by providing important insights into the foreign exchange policy in Brazil.

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Notes

  1. For illustration, the Brazilian Finance Minister Guido Mantega coined the term “currency war” in 2010 referring to unconventional monetary policies of the Federal Reserve that triggered a tsunami of capital flows toward emerging markets, resulting in inflation, currency appreciation, and loss of external competitiveness (Eichengreen 2013).

  2. The data from the Brazilian Agency are available in http://www.anp.gov.br/publicacoes/anuario-estatistico/5237-anuario-estatistico-2019http://www.anp.gov.br/publicacoes/anuario-estatistico/5237-anuario-estatistico-2019.

  3. The data are available at http://comexstat.mdic.gov.br/. SITC code: 3330.

  4. Unless otherwise noted, Brazilian currency, Brazilian real and real exchange rate are interchangeable terms, expressing the Brazilian real against the US dollar measured in real terms. Section 2.2 presents the variable details and sources.

  5. The literature has often shown the negative relationship between country risk and commodity prices (Bastourre et al. 2012; Aslam et al. 2016; Hilscher and Nosbusch 2010; Bouri et al. 2016; Barone and Descalzi 2012).

  6. Moreover, the proxy of liquidity (M2) for Japan is available since 2003 in the Bank of Japan.

  7. We use the VAR lag order selection criteria for choosing the optimal lag length. The tests indicate one or twelve lags. The VAR residual serial correlation LM test can not reject the null hypothesis of no serial correlation at lags 1 to 12 for j = 4. Therefore, we chose four lags to ensure no serial correlation and parsimony. The VAR satisfies the stability condition. Section 5 contains a sensitivity exercise to an alternative lag number.

  8. The results of formal unit root and cointegration tests are shown in Section 2.3. All the variables are difference-stationary, except for the aggregate demand, which is stationary in level. In this paper, we use the index of Kilian (2009) as a proxy of aggregate demand (details in Section 2.2). The Kilian’s real activity index is a business cycle index and, hence, must not be differenced or otherwise transformed, according with Kilian and Zhou (2018).

  9. Section 5 contains a robustness exercise to the ordering of crude oil prices.

  10. The data are available in the Kilian’s website (https://sites.google.com/site/lkilian2019/).

  11. Namely, the data are from the Central Bank of Brazil, European Central Bank, Bank of Japan, Reserve Bank of India, FRED, Central Bank of Russian Federation, Bank of England and CEIC database.

  12. The currencies used to convert monetary aggregates in local currency to US dollars are Brazilian real, Chinese yuan, Euro, Indian rupees, Japanese yen, Russian ruble and British pound.

  13. Lee and Strazicich (2003) stress that is desirable to allow the possibility of a structural break in a unit root process, since that the rejection of the null might indicate evidence of a trend-stationary time series with breaks, when the series is difference-stationary with breaks.

  14. Section 5 contains a sensitivity to the periods in which Brazil has been a net exporter of crude oil. Figure B1 shows these periods.

  15. The source is the FRED.

  16. Chen et al. (2016) used 6 lags in an SVAR model with monthly data to analyze the relationship between oil prices and US dollar exchange rates. Baumeister and Peersman (2013) and Baumeister and Peersman (2012) argue that including 4 lags in a model with quarterly data (12 months) allows for sufficient dynamics in the system.

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Acknowledgements

The authors gratefully acknowledge the PNPD/CAPES scholarship program. Leonardo Bornacki de Mattos gratefully acknowledges the financial support received from the National Council for Scientific and Technological Development (CNPq) through a PQ-2 research productivity grant.

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Authors and Affiliations

  1. Department of Agricultural Economics, Federal University of Viçosa, Potsch Magalhães - R. Purdue, s/n - Campus Universitário, Viçosa, MG, 36570-900, Brazil

    Rodrigo da Silva Souza & Leonardo Bornacki de Mattos

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  1. Rodrigo da Silva Souza
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  2. Leonardo Bornacki de Mattos
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Correspondence to Rodrigo da Silva Souza.

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da Silva Souza, R., de Mattos, L.B. Oil price shocks and global liquidity: macroeconomic effects on the Brazilian real. Int Econ Econ Policy 19, 761–781 (2022). https://doi.org/10.1007/s10368-022-00532-x

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