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Commodity prices under the threat of operational disruptions: Labor strikes at copper mines

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  • Fernandez, Viviana
  • Pastén-Henríquez, Boris
  • Tapia-Griñen, Pablo
  • Wagner, Rodrigo

Abstract

The threat of short-term supply disruptions may matter for commodity prices, although their magnitude is hard to detect, for example due to anticipation, storage and to the relatively short duration of disruption events. This article explores global commodity returns for copper around labor strikes in Chile mines between 1910 and 2010. In the five days around strikes, copper display cumulative abnormal returns (CAR) close to 200 basis points (bps). Consistent with the threat of supply disruptions, the effect comes almost fully from strikes at larger mines (CAR≈ 500 bps). Moreover, the price-increasing effect of strikes is stronger when copper inventories are scarce, as measured by the interest-adjusted basis. Despite strikes being transitory events, we also find a mirroring appreciation of the USD/CLP commodity currency.

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  • Fernandez, Viviana & Pastén-Henríquez, Boris & Tapia-Griñen, Pablo & Wagner, Rodrigo, 2023. "Commodity prices under the threat of operational disruptions: Labor strikes at copper mines," Journal of Commodity Markets, Elsevier, vol. 32(C).
  • Handle: RePEc:eee:jocoma:v:32:y:2023:i:c:s2405851323000557
    DOI: 10.1016/j.jcomm.2023.100365
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    as
    1. Ben Jacobsen & Ben R. Marshall & Nuttawat Visaltanachoti, 2019. "Stock Market Predictability and Industrial Metal Returns," Management Science, INFORMS, vol. 65(7), pages 3026-3042, July.
    2. Cashin, Paul & Cespedes, Luis F. & Sahay, Ratna, 2004. "Commodity currencies and the real exchange rate," Journal of Development Economics, Elsevier, vol. 75(1), pages 239-268, October.
    3. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    4. Philip Hans Franses & Paul Kofman, 1991. "An empirical test for parities between metal prices at the LME," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 11(6), pages 729-736, December.
    5. repec:dau:papers:123456789/14980 is not listed on IDEAS
    6. Juan Ignacio Guzmán & Enrique Silva, 2018. "Copper price determination: fundamentals versus non-fundamentals," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 31(3), pages 283-300, October.
    7. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    8. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    9. Ahmed, Rashad, 2020. "Commodity currencies and causality: Some high-frequency evidence," Economics Letters, Elsevier, vol. 189(C).
    10. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
    11. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    12. Stuermer, Martin, 2017. "Industrialization and the demand for mineral commodities," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 16-27.
    13. Anders Fredriksson & Gustavo Magalhães de Oliveira, 2019. "Impact evaluation using Difference-in-Differences," RAUSP Management Journal, Emerald Group Publishing Limited, vol. 54(4), pages 519-532, September.
    14. Ahmed, Rashad, 2019. "Commodity Currencies and Causality: Some High-Frequency Evidence," MPRA Paper 98319, University Library of Munich, Germany, revised 25 Jan 2020.
    15. Martin Stuermer, 2022. "Non-renewable resource extraction over the long term: empirical evidence from global copper production," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 617-625, December.
    16. Kowalewski, Oskar & Śpiewanowski, Piotr, 2020. "Stock market response to potash mine disasters," Journal of Commodity Markets, Elsevier, vol. 20(C).
    17. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    18. Fernandez, Viviana, 2021. "Copper mining in Chile and its regional employment linkages," Resources Policy, Elsevier, vol. 70(C).
    19. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    20. Franklin M. Fisher & Paul H. Cootner & Martin N. Baily, 1972. "An Econometric Model of the World Copper Industry," Bell Journal of Economics, The RAND Corporation, vol. 3(2), pages 568-609, Autumn.
    21. Fernandez, Viviana, 2015. "Commodity price excess co-movement from a historical perspective: 1900–2010," Energy Economics, Elsevier, vol. 49(C), pages 698-710.
    22. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    23. Puhani, Patrick A., 2012. "The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models," Economics Letters, Elsevier, vol. 115(1), pages 85-87.
    24. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2010. "The macroeconomic determinants of volatility in precious metals markets," Resources Policy, Elsevier, vol. 35(2), pages 65-71, June.
    25. Diego R. Känzig, 2021. "The Macroeconomic Effects of Oil Supply News: Evidence from OPEC Announcements," American Economic Review, American Economic Association, vol. 111(4), pages 1092-1125, April.
    26. Diego R. Känzig, 2023. "The Unequal Economic Consequences of Carbon Pricing," NBER Working Papers 31221, National Bureau of Economic Research, Inc.
    27. Zaremba, Adam & Mikutowski, Mateusz & Szczygielski, Jan Jakub & Karathanasopoulos, Andreas, 2021. "The alpha momentum effect in commodity markets," Energy Economics, Elsevier, vol. 93(C).
    28. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    29. Fernandez, Viviana, 2020. "The predictive power of convenience yields," Resources Policy, Elsevier, vol. 65(C).
    30. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    31. Karanasos, Menelaos & Menla Ali, Faek & Margaronis, Zannis & Nath, Rajat, 2018. "Modelling time varying volatility spillovers and conditional correlations across commodity metal futures," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 246-256.
    32. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    33. Belousova, Julia & Dorfleitner, Gregor, 2012. "On the diversification benefits of commodities from the perspective of euro investors," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2455-2472.
    34. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    35. Schipper, K & Thompson, R, 1983. "The Impact Of Merger-Related Regulations On The Shareholders Of Acquiring Firms," Journal of Accounting Research, Wiley Blackwell, vol. 21(1), pages 184-221.
    36. Wagner, Rodrigo, 2018. "Can the market value state-owned enterprises without privatizing them? An application to natural resources companies," Resources Policy, Elsevier, vol. 59(C), pages 282-290.
    37. Reus, Lorenzo & Pagnoncelli, Bernardo & Armstrong, Margaret, 2019. "Better management of production incidents in mining using multistage stochastic optimization," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    38. Pirrong, Craig, 2017. "The economics of commodity market manipulation: A survey," Journal of Commodity Markets, Elsevier, vol. 5(C), pages 1-17.
    39. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    40. Hansen, Erwin & Wagner, Rodrigo, 2017. "Stockpiling cash when it takes time to build: Exploring price differentials in a commodity boom," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 197-212.
    41. Al-Yahyaee, Khamis Hamed & Rehman, Mobeen Ur & Wanas Al-Jarrah, Idries Mohammad & Mensi, Walid & Vo, Xuan Vinh, 2020. "Co-movements and spillovers between prices of precious metals and non-ferrous metals: A multiscale analysis," Resources Policy, Elsevier, vol. 67(C).
    42. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    43. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    44. Borkowski, Bolesław & Krawiec, Monika & Karwański, Marek & Szczesny, Wiesław & Shachmurove, Yochanan, 2021. "Modeling garch processes in base metals returns using panel data," Resources Policy, Elsevier, vol. 74(C).
    45. Geman, Hélyette & Smith, William O., 2013. "Theory of storage, inventory and volatility in the LME base metals," Resources Policy, Elsevier, vol. 38(1), pages 18-28.
    46. Gilbert, Christopher L., 2021. "Monopolistic supply management in world metals markets: How large was Mount Isa?," Journal of Commodity Markets, Elsevier, vol. 21(C).
    47. Schnebele, Emily & Jaiswal, Kishor & Luco, Nicolas & Nassar, Nedal T., 2019. "Natural hazards and mineral commodity supply: Quantifying risk of earthquake disruption to South American copper supply," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    48. Morales, Lucía & Andreosso-O'Callaghan, Bernadette, 2011. "Comparative analysis on the effects of the Asian and global financial crises on precious metal markets," Research in International Business and Finance, Elsevier, vol. 25(2), pages 203-227, June.
    49. Fernandez, Viviana, 2016. "Futures markets and fundamentals of base metals," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 215-229.
    50. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    51. Iyke, Bernard Njindan & Ho, Sin-Yu, 2021. "Stock return predictability over four centuries: The role of commodity returns," Finance Research Letters, Elsevier, vol. 40(C).
    52. Fama, Eugene F, et al, 1969. "The Adjustment of Stock Prices to New Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 1-21, February.
    53. Wing H. Chan & Denise Young, 2006. "Jumping hedges: An examination of movements in copper spot and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(2), pages 169-188, February.
    54. Manley, Ross L. & Alonso, Elisa & Nassar, Nedal T., 2022. "A model to assess industry vulnerability to disruptions in mineral commodity supplies," Resources Policy, Elsevier, vol. 78(C).
    55. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    56. Antonio Spilimbergo, 2002. "Copper and the Chilean Economy, 1960-98," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 5(2), pages 115-126.
    57. Agyei-Ampomah, Sam & Gounopoulos, Dimitrios & Mazouz, Khelifa, 2014. "Does gold offer a better protection against losses in sovereign debt bonds than other metals?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 507-521.
    58. Karafiath, Imre, 1988. "Using Dummy Variables in the Event Methodology," The Financial Review, Eastern Finance Association, vol. 23(3), pages 351-357, August.
    59. Mei-Se, Chien & Shu-Jung, Chang Lee & Chien-Chiang, Lee, 2018. "Time-varying co-movement of the prices of three metals and oil: Evidence from recursive cointegration," Resources Policy, Elsevier, vol. 57(C), pages 186-195.
    60. Michael Pedersen, 2019. "The impact of commodity price shocks in a copper-rich economy: the case of Chile," Empirical Economics, Springer, vol. 57(4), pages 1291-1318, October.
    61. Eric C. Chang & Chao Chen & Son‐Nan Chen, 1990. "Risk and return in copper, platinum, and silver futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 10(1), pages 29-39, February.
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