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Climate variability and the volatility of global maize and soybean prices

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Abstract

Volatility in the price of agricultural commodities is one of the main factors affecting food security. Many studies have analyzed agricultural market instability from different points of view, but the effect of climate oscillations on agricultural price volatility has been little studied. Climate anomalies, and in particular extreme events, can alter agricultural yields and stocks with related effects on prices. This paper presents a Volatility Impulse Response Function (VIRF) from a multivariate GARCH model to investigate the effects of variability in climatic shocks (El Niño/Southern Oscillation - ENSO) on international maize and soybean price volatility from 1960 to 2014. For both commodities, VIRF analysis was conducted splitting the effect of El Niño and La Niña events according to Spring-Summer and Autumn-Winter meteorological seasons. Both events increase expected price volatility of maize, showing the strongest impact during the El Niño phase in Spring-Summer. Soybean price volatility tends to slightly decrease during Autumn-Winter meteorological seasons and to increase during the Spring-Summer period. To minimize the impact of ENSO events on commodity price volatility, various measures can be taken, both political and technical. Financial aspects should also be considered. It is possible that financial agents can use the ENSO index as information for trading activity, creating a new link between this index and volatility in commodity prices.

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Notes

  1. http://databank.worldbank.org/data

  2. To be comparable with the other index and for compatibility with the economic literature related to ENSO events and agricultural commodity prices in Fig. 1, Fig. 2 and in the empirical application, the SOI index was multiplied by −1, so that positive variation in the index corresponds to El Niño events while negative shock corresponds to La Niña.

  3. To verify the robustness of results, the model with the SST index was also estimated obtaining similar results in the sign of the estimated coefficient and in the VIRF pattern.

  4. http://www.cpc.ncep.noaa.gov/data/indices/soi

  5. Anomalies were calculated for the base period 1981–2010

  6. Specifically, the following specification was used for commodity prices: 100 x log (P t /P t-1 ).

  7. The GARCH model was also used as a test in the empirical application with the mean equation modeled as an AR(1), obtaining very similar results in the covariance matrices, but the residual diagnostic was not significant.

  8. A restricted version of the model was also tested using a 21 = b 21 = 0 and a 12 ≠ 0 and b 12 ≠ 0 (i.e. a triangular BEKK) obtaining analogous results.

  9. Considering the previous results of the Zivot and Andrews (ZA) test and the price dynamics of many agricultural commodities recorded during 1973/74 (Eckstein and Heien 1978), when soybean prices experienced an anomalous peak - just in correspondence with the occurrence of a La Niña event - I decided not to include these two years in the soybean VIRF analysis.

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Acknowledgements

I am grateful to two anonymous reviewers, an Associate Editor and the Deputy Editor in Chief, whose comments have greatly improved the manuscript. A previous version was presented at the 29th International Conference of Agricultural Economists, August 9-14, 2015, in Milan. Participants made welcome comments. All errors and opinions are mine.

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  1. Department of Environmental Science and Policy, University of Milan, Via Celoria, 2 -, 20133, Milano, Italy

    Massimo Peri

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Peri, M. Climate variability and the volatility of global maize and soybean prices. Food Sec. 9, 673–683 (2017). https://doi.org/10.1007/s12571-017-0702-2

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