IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2010.03350.html
   My bibliography  Save this paper

Modeling the commodity prices of base metals in Indian commodity market using a Higher Order Markovian Approach

Author

Listed:
  • Suryadeepto Nag
  • Sankarshan Basu
  • Siddhartha P. Chakrabarty

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 offer 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 the volatility in the Asian market during the COVID-19 outbreak.

Suggested Citation

  • Suryadeepto Nag & Sankarshan Basu & Siddhartha P. Chakrabarty, 2020. "Modeling the commodity prices of base metals in Indian commodity market using a Higher Order Markovian Approach," Papers 2010.03350, arXiv.org.
    • Handle: RePEc:arx:papers:2010.03350
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2010.03350
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chia‐Hsien Tang & Yen‐Hsien Lee & Hung‐Chun Liu & Guan‐Gzhe Zeng, 2024. "Exploring the unpredictable nature of climate policy uncertainty: An empirical analysis of its impact on commodity futures returns in the United States," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1277-1292, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jilong Chen & Christian Ewald & Ruolan Ouyang & Sjur Westgaard & Xiaoxia Xiao, 2022. "Pricing commodity futures and determining risk premia in a three factor model with stochastic volatility: the case of Brent crude oil," Annals of Operations Research, Springer, vol. 313(1), pages 29-46, June.
    2. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    3. Back, Janis & Prokopczuk, Marcel & Rudolf, Markus, 2013. "Seasonality and the valuation of commodity options," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 273-290.
    4. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, October.
    5. Mirantes, Andrés García & Población, Javier & Serna, Gregorio, 2013. "The stochastic seasonal behavior of energy commodity convenience yields," Energy Economics, Elsevier, vol. 40(C), pages 155-166.
    6. Max F. Schöne & Stefan Spinler, 2017. "A four-factor stochastic volatility model of commodity prices," Review of Derivatives Research, Springer, vol. 20(2), pages 135-165, July.
    7. Na Jin & Sergio Lence & Chad Hart & Dermot Hayes, 2012. "The Long-Term Structure of Commodity Futures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 718-735.
    8. Iván Blanco, Juan Ignacio Peña, and Rosa Rodriguez, 2018. "Modelling Electricity Swaps with Stochastic Forward Premium Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    9. repec:dau:papers:123456789/5470 is not listed on IDEAS
    10. Richter, Martin & Sørensen, Carsten, 2002. "Stochastic Volatility and Seasonality in Commodity Futures and Options: The Case of Soybeans," Working Papers 2002-4, Copenhagen Business School, Department of Finance.
    11. Ke Du, 2013. "Commodity Derivative Pricing Under the Benchmark Approach," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2013, January-A.
    12. Gareth William Peters & Mark Briers & Pavel Shevchenko & Arnaud Doucet, 2013. "Calibration and Filtering for Multi Factor Commodity Models with Seasonality: Incorporating Panel Data from Futures Contracts," Methodology and Computing in Applied Probability, Springer, vol. 15(4), pages 841-874, December.
    13. Wolfgang Bühler & Olaf Korn & Rainer Schöbel, 2005. "Hedging Long-Term Forwards with Short-Term Futures: A Two-Regime Approach," Review of Derivatives Research, Springer, vol. 7(3), pages 185-212, October.
    14. Ke Tang, 2012. "Time-varying long-run mean of commodity prices and the modeling of futures term structures," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 781-790, April.
    15. repec:dau:papers:123456789/95 is not listed on IDEAS
    16. Dannenberg, Henry & Ehrenfeld, Wilfried, 2010. "Stochastic Income Statement Planning and Emissions Trading," IWH Discussion Papers 4/2010, Halle Institute for Economic Research (IWH).
    17. Delphine Lautier & Alain Galli, 2010. "Dynamic hedging strategies: an application to the crude oil market," Post-Print halshs-00640802, HAL.
    18. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.
    19. Sévi, Benoît, 2015. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Economic Modelling, Elsevier, vol. 44(C), pages 243-251.
    20. Ioannis Kyriakou & Nikos K. Nomikos & Nikos C. Papapostolou & Panos K. Pouliasis, 2016. "Affine†Structure Models and the Pricing of Energy Commodity Derivatives," European Financial Management, European Financial Management Association, vol. 22(5), pages 853-881, November.
    21. Cheng, Benjamin & Nikitopoulos, Christina Sklibosios & Schlögl, Erik, 2018. "Pricing of long-dated commodity derivatives: Do stochastic interest rates matter?," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 148-166.
    22. repec:dau:papers:123456789/1245 is not listed on IDEAS
    23. Finbarr Murphy & Ehud Ronn, 2015. "The valuation and information content of options on crude-oil futures contracts," Review of Derivatives Research, Springer, vol. 18(2), pages 95-106, July.

    More about this item

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2010.03350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.