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Speculative incentives to hoard aluminum: Relationship between capital gains and inventories

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  • Kim, Soohyeon
  • Kim, Jihyo
  • Heo, Eunnyeong
Abstract
This study explores the relationship between the capital gains and inventory in the aluminum market. We provide analytical evidence concerning how the increase in opportunities for speculative incentives from aluminum inventories caused an unprecedented purchase of inventories between 2009 and 2014. In this study, adjusted basis represents monetary and non-monetary value gained from holding inventory; if positive, this shows capital gains occurring from inventory, and if negative, the absolute value of the adjusted basis represents the convenience yield. Unlike the previous literature on convenience yields which assumes uniform relationship between adjusted basis and inventory throughout the period, we open the possibility of time-variance between variables by using dynamic linear model (DLM) and the time-varying parameter-vector autoregression-stochastic volatility (TVP-VAR-SV). A series of analysis in this study suggests clear evidence of inventory having been increased in pursuit of the monetary profit opportunities in the aluminum market.

Suggested Citation

  • Kim, Soohyeon & Kim, Jihyo & Heo, Eunnyeong, 2021. "Speculative incentives to hoard aluminum: Relationship between capital gains and inventories," Resources Policy, Elsevier, vol. 70(C).
  • Handle: RePEc:eee:jrpoli:v:70:y:2021:i:c:s0301420720309326
    DOI: 10.1016/j.resourpol.2020.101901
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    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Sims, Christopher A., 1988. "Bayesian skepticism on unit root econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 463-474.
    3. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    4. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    5. Nakajima, Jouchi & Kasuya, Munehisa & Watanabe, Toshiaki, 2011. "Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy," Journal of the Japanese and International Economies, Elsevier, vol. 25(3), pages 225-245, September.
    6. Bosch, David & Pradkhan, Elina, 2015. "The impact of speculation on precious metals futures markets," Resources Policy, Elsevier, vol. 44(C), pages 118-134.
    7. Nicholas Kaldor, 1939. "Speculation and Economic Stability," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 7(1), pages 1-27.
    8. Kolodziej, Marek & Kaufmann, Robert K. & Kulatilaka, Nalin & Bicchetti, David & Maystre, Nicolas, 2014. "Crude oil: Commodity or financial asset?," Energy Economics, Elsevier, vol. 46(C), pages 216-223.
    9. Fernandez, Viviana, 2020. "The predictive power of convenience yields," Resources Policy, Elsevier, vol. 65(C).
    10. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    11. Lester G. Telser, 1958. "Futures Trading and the Storage of Cotton and Wheat," Journal of Political Economy, University of Chicago Press, vol. 66(3), pages 233-233.
    12. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    13. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    14. Rafiq, M.S. & Mallick, S.K., 2008. "The effect of monetary policy on output in EMU3: A sign restriction approach," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1756-1791, December.
    15. Geman, Hélyette & Ohana, Steve, 2009. "Forward curves, scarcity and price volatility in oil and natural gas markets," Energy Economics, Elsevier, vol. 31(4), pages 576-585, July.
    16. 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.
    17. Andreasson, Pierre & Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2016. "Impact of speculation and economic uncertainty on commodity markets," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 115-127.
    18. Fernandez, Viviana, 2016. "Futures markets and fundamentals of base metals," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 215-229.
    19. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    20. Irwin, Scott H. & Sanders, Dwight R., 2012. "Testing the Masters Hypothesis in commodity futures markets," Energy Economics, Elsevier, vol. 34(1), pages 256-269.
    21. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, September.
    22. Marco Haase & Yvonne Seiler Zimmermann & Heinz Zimmermann, 2019. "Permanent and transitory price shocks in commodity futures markets and their relation to speculation," Empirical Economics, Springer, vol. 56(4), pages 1359-1382, April.
    23. Working, Holbrook, 1960. "Speculation on Hedging Markets," Food Research Institute Studies, Stanford University, Food Research Institute, vol. 1(2), pages 1-36.
    24. Kim, Soohyeon & Kim, Jihyo & Heo, Eunnyeong, 2017. "Convenience yield of accessible inventories and imports: A case study of the Chinese copper market," Resources Policy, Elsevier, vol. 52(C), pages 277-283.
    25. Omura, Akihiro & Todorova, Neda & Li, Bin & Chung, Richard, 2015. "Convenience yield and inventory accessibility: Impact of regional market conditions," Resources Policy, Elsevier, vol. 44(C), pages 1-11.
    26. Imad A. Moosa & Nabeel E. Al‐Loughani, 1995. "The effectiveness of arbitrage and speculation in the crude oil futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(2), pages 167-186, April.
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