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Impact of Derivatives Trading on Emerging Capital Markets: A Note on Expiration Day Effects in India

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  • Sumon Bhaumik
  • Suchismita Bose

Abstract

The impact of expiration of derivatives contracts on the underlying cash market ??? on trading volumes, returns and volatility of returns ??? has been studied in various contexts. We use an AR-GARCH model to analyse the impact of expiration of derivatives contracts on the cash market at the largest stock exchange in India, an important emerging capital market. Our results indicate that trading volumes were significantly higher on expiration days and during the five days leading up to expiration days (???expiration weeks???), compared with nonexpiration days (weeks). We also find significant expiration day effects on daily returns to the market index, and on the volatility of these returns. Finally, our analysis indicates that it might be prudent to undertake analysis of expiration day effects (or other events) using methodologies that model the underlying data generating process, rather than depend on comparison of mean and median alone.

Suggested Citation

  • Sumon Bhaumik & Suchismita Bose, 2007. "Impact of Derivatives Trading on Emerging Capital Markets: A Note on Expiration Day Effects in India," William Davidson Institute Working Papers Series wp863, William Davidson Institute at the University of Michigan.
    • Handle: RePEc:wdi:papers:2007-863
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Christian Schlag, 1996. "Expiration day effects of stock index derivatives in Germany," European Financial Management, European Financial Management Association, vol. 2(1), pages 69-95, March.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    5. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
    6. Per Alkeback & Niclas Hagelin, 2004. "Expiration day effects of index futures and options: evidence from a market with a long settlement period," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 385-396.
    7. N/A, 2004. "Index for 2004," European Union Politics, , vol. 5(4), pages 511-512, December.
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    Cited by:

    1. Rachna Mahalwala, 2016. "A Study of Expiration-day Effects of Index Derivatives Trading in India," Metamorphosis: A Journal of Management Research, , vol. 15(1), pages 10-19, June.
    2. Bhattacharyya, Surajit & Saxena, Arunima, 2008. "Stock Futures Introduction & Its Impact on Indian Spot Market," MPRA Paper 15250, University Library of Munich, Germany.
    3. Chhabra, Damini & Gupta, Mohit, 2022. "Calendar anomalies in commodity markets for natural resources: Evidence from India," Resources Policy, Elsevier, vol. 79(C).

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    More about this item

    Keywords

    derivatives contracts; expiration day effect; India;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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