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Modeling the Commodity Prices of Base Metals in Indian Commodity Market Using a Higher Order Markovian Approach

Author

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  • Suryadeepto Nag

    (Indian Institute of Science Education and Research Pune)

  • Sankarshan Basu

    (Indian Institute of Management Bangalore)

  • Siddhartha P. Chakrabarty

    (Indian Institute of Technology Guwahati)

Abstract

A higher order Markovian (HOM) model to capture the dynamics of commodity prices is proposed as an alternative to a Markovian model. In particular, the order of the former model, is taken to be the delay, in the response of the industry, to the market information. This is then empirically analyzed for the prices of copper mini and four other bases metals, namely aluminum, lead, nickel and zinc, in the Indian commodities market. In case of copper mini, the usage of the HOM approach consistently offered improvement, over the Markovian approach, in terms of the errors in forecasting. Similar trends were observed for the other base metals considered, with the exception of aluminum, which can be attributed to the volatility in the Asian market during the COVID-19 outbreak.

Suggested Citation

  • Suryadeepto Nag & Sankarshan Basu & Siddhartha P. Chakrabarty, 2022. "Modeling the Commodity Prices of Base Metals in Indian Commodity Market Using a Higher Order Markovian Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 159-171, March.
  • Handle: RePEc:spr:jqecon:v:20:y:2022:i:1:d:10.1007_s40953-021-00258-8
    DOI: 10.1007/s40953-021-00258-8
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    References listed on IDEAS

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    1. Jean-Thomas Bernard & Lynda Khalaf & Maral Kichian & Sebastien Mcmahon, 2008. "Forecasting commodity prices: GARCH, jumps, and mean reversion," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 279-291.
    2. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    3. Chad E. Hart & Sergio H. Lence & Dermot J. Hayes & Na Jin, 2016. "Price Mean Reversion, Seasonality, and Options Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(3), pages 707-725.
    4. Wai Ki Ching & Eric S. Fung & Michael K. Ng, 2004. "Higher‐order Markov chain models for categorical data sequences," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 557-574, June.
    5. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    6. Ali, Mohsin & Alam, Nafis & Rizvi, Syed Aun R., 2020. "Coronavirus (COVID-19) — An epidemic or pandemic for financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    7. Hilliard, Jimmy E. & Reis, Jorge, 1998. "Valuation of Commodity Futures and Options under Stochastic Convenience Yields, Interest Rates, and Jump Diffusions in the Spot," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(1), pages 61-86, March.
    8. Wai-Ki Ching & Michael K. Ng, 2006. "Markov Chains: Models, Algorithms and Applications," International Series in Operations Research and Management Science, Springer, number 978-0-387-29337-0, April.
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    More about this item

    Keywords

    Commodity prices; Copper mini; Higher order Markovian; Estimation;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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