Indian Commodity Derivatives Market : Structural Breaks and Price Discovery

Authors

  •   U. Sarita Singha Research Scholar, Department of Humanities and Social Sciences, National Institute of Technology Silchar, NIT Road, Fakiratilla, Silchar - 788 010, Assam
  •   N. B. Singh Professor, Department of Humanities and Social Sciences, National Institute of Technology Silchar, NIT Road, Fakiratilla, Silchar - 788 010, Assam
  •   Kelvin Mutum Assistant Professor (Corresponding Author), Department of Humanities and Social Sciences, National Institute of Technology Meghalaya, Bijni Complex, Laitumkhrah, Shillong - 793 003, Meghalaya

DOI:

https://doi.org/10.17010/ijf/2024/v18i8/174240

Keywords:

market efficiency

, agricultural commodities, causality test, derivatives, multiple breakpoints.

JEL Classification Codes

, C32, G13, G14, Q02, Q11

Paper Submission Date

, September 25, 2023, Paper sent back for Revision, April 23, 2024, Paper Acceptance Date, June 10, Paper Published Online, August 14, 2024

Abstract

Purpose : The purpose of this research was to determine structural breakpoints and examine the causal relationship between the spot and futures markets for 12 commodities that are traded in India.

Methodology : Data were obtained from the Multi Commodity Exchange of India Limited (MCX) website for both futures and spot price series of 12 commodities, including agricultural commodities (crude palm oil (CPO), mentha oil, and cotton), energy commodities (natural gas and crude oil), precious metals (silver and gold), and base metals (lead, zinc, aluminum, copper, and nickel). Structural breaks were identified using a multiple breakpoint test, and the price series were segmented into sub-periods. Subsequently, the Granger causality test was conducted within a vector autoregressive (VAR) framework to examine causal relationships between the spot and futures markets. This analysis was conducted for the entire study period and for sub-periods identified by structural breaks.

Findings : Bidirectional relationships were observed for the whole period across all commodities, indicating market efficiency. Nonetheless, in certain sub-periods, unidirectional causality was noted, indicating potential structural fractures brought on by events influencing market efficiency on the political or economic front.

Practical Implications : The findings of this study are valuable for various stakeholders, including individual and institutional investors, policymakers shaping regulatory frameworks, market regulators, exchange facilitators, and other relevant authorities. These insights contributed to informed decision-making, robust policy formulation, improved regulatory oversight, and the enhancement of market integrity and stability.

Originality : This study contributed to the literature by considering structural breakpoints in the analysis of price discovery and market efficiency in the Indian commodity derivatives market, an aspect that has been relatively underexplored in previous studies.

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Published

2024-08-14

How to Cite

Singha, U. S., Singh, N. B., & Mutum, K. (2024). Indian Commodity Derivatives Market : Structural Breaks and Price Discovery. Indian Journal of Finance, 18(8), 8–21. https://doi.org/10.17010/ijf/2024/v18i8/174240

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Section

Articles

References

Ali, J., & Gupta, K. B. (2011). Efficiency in agricultural commodity futures markets in India: Evidence from cointegration and causality tests. Agricultural Finance Review, 71(2), 162–178. https://doi.org/10.1108/00021461111152555

Andrews, D. W. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856. https://doi.org/10.2307/2951764

Bai, J. (1997). Estimating multiple breaks one at a time. Econometric Theory, 13(3), 315–352. https://doi.org/10.1017/s0266466600005831

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. https://doi.org/10.2307/2998540

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605. https://doi.org/10.2307/1910133

Gupta, S., Choudhary, H., & Agarwal, D. R. (2018). An empirical analysis of market efficiency and price discovery in the Indian commodity market. Global Business Review, 19(3), 771–789. https://doi.org/10.1177/0972150917713882

Inani, S. K. (2018). Price discovery and efficiency of Indian agricultural commodity futures market: An empirical investigation. Journal of Quantitative Economics, 16(1), 129–154. https://doi.org/10.1007/s40953-017-0074-7

Inoue, T., & Hamori, S. (2014). Market efficiency of commodity futures in India. Applied Economics Letters, 21(8), 522–527. https://doi.org/10.1080/13504851.2013.872751

Irfan, M., & Hooda, J. (2017). An empirical study of price discovery in commodities futures market. Indian Journal of Finance, 11(3), 41–57. https://doi.org/10.17010/ijf/2017/v11i3/111648

Joarder, S., & Mukherjee, D. (2021). The lead–lag relationship between futures and spot price—A case of the oil and oilseed contracts traded on Indian exchange. Arthaniti: Journal of Economic Theory and Practice, 20(1), 7–33. https://doi.org/10.1177/0976747919842689

Kaura, R., & Rajput, N. (2021). Future–spot relationship in commodity market: A comparison across commodity segments in India. Global Business Review. https://doi.org/10.1177/09721509211017291

Kaura, R., Kishor, N., & Rajput, N. (2019). Arbitrage, error correction, and causality: Case of highly traded agricultural commodities in India. Indian Journal of Finance, 13(9), 7–21. https://doi.org/10.17010/ijf/2019/v13i9/147095

Kumar, B., & Pandey, A. (2013). Market efficiency in Indian commodity futures markets. Journal of Indian Business Research, 5(2), 101–121. https://doi.org/10.1108/17554191311320773

Lethesh, M., & Reddy, C. V. (2023). Price discovery mechanism in the Indian agricultural commodity futures market – An empirical analysis. Indian Journal of Finance, 17(12), 8–25. https://doi.org/10.17010/ijf/2023/v17i12/172823

Manogna, R. L., & Mishra, A. K. (2020). Price discovery and volatility spillover: An empirical evidence from spot and futures agricultural commodity markets in India. Journal of Agribusiness in Developing and Emerging Economies, 10(4), 447–473. https://doi.org/10.1108/jadee-10-2019-0175

Multi Commodity Exchange of India Limited. (2022). Commodity insights year book - 2022. https://www.mcxindia.com/docs/default-source/about-us/commodity-insights-yearbook/2022/commodity-insights-yearbook-2022---statistics.pdf?sfvrsn=4c33ba90_2

Parthasarathy, S. (2019). Revisiting the weak form efficiency with structural breaks: Evidence from the Indian stock market. Indian Journal of Finance, 13(10), 7–21. https://doi.org/10.17010/ijf/2019/v13i10/147744

Peri, M., Baldi, L., & Vandone, D. (2013). Price discovery in commodity markets. Applied Economics Letters, 20(4), 397–403. https://doi.org/10.1080/13504851.2012.709590

Perumandla, S., & Kurisetti, P. (2018). Time - varying correlations, causality, and volatility linkages of Indian commodity and equity markets: Evidence from DCC - GARCH. Indian Journal of Finance, 12(9), 21–40. https://doi.org/10.17010/ijf/2018/v12i9/131558

Purohit, H., Bodhanwala, S., & Choudhary, N. (2015). Empirical study on price discovery role in non-precious metals market in India. SAMVAD: SIBM Pune Research Journal, 10, 15–25. https://samvad.sibmpune.edu.in/index.php/samvad/article/view/98321/71458

Quandt, R. E. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American Statistical Association, 55(290), 324–330. https://doi.org/10.1080/01621459.1960.10482067

Rani, P., & Kumar, S. (2023). Agricultural commodities' research associated with economic activities in the past 20 years: A bibliometric analysis. Indian Journal of Research in Capital Markets, 10(3–4), 40–61. https://doi.org/10.17010/ijrcm/2023/v10i3-4/173430

Sehgal, S., Rajput, N., & Diesting, F. (2013). Price discovery and volatility spillover: Evidence from Indian commodity markets. Available at SSRN. https://ssrn.com/abstract=2149790

Seth, N., & Sidhu, A. (2021). Price discovery and volatility spillover for Indian energy futures market in the pre- and post-crisis periods. Indian Journal of Finance, 15(8), 24–39. https://doi.org/10.17010/ijf/2021/v15i8/165816

Sonia. (2023). Indian commodity derivatives market: A performance review. Indian Journal of Research in Capital Markets, 10(2), 20–29. https://doi.org/10.17010/ijrcm/2023/v10i2/173302

Zarei, A., Ariff, M., Hook, L. S., & Nassir, A. M. (2015). Identifying multiple structural breaks in exchange rate series in a finance research. Pertanika Journal of Social Sciences and Humanities, 23(S), Article ID JSSH-S0026-2015. http://www.pertanika.upm.edu.my/pjssh/browse/special-issue?article=JSSH-S0026-2015