IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v18y2011i6p567-574.html
   My bibliography  Save this article

Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures

Author

Listed:
  • Pratap Chandra Pati
  • Prabina Rajib

Abstract

Using 5-min intraday transaction prices, this study investigates the relationship between the National Stock Exchange (NSE) S&P CNX Nifty futures and its underlying spot index in terms of both return and volatility. By applying Johansen-Juselius (J-J) cointegration analysis, we find evidence of single common stochastic trend, to which spot and futures prices move together in a long-run equilibrium path. The vector error correction model (VECM) and Granger causality test find that there is unidirectional causality running from futures to spot market. To examine the volatility spillovers between the markets, this study has used bivariate Generalized Autoregressive Conditional Heteroscedastic (GARCH) (1, 1) model with Baba, Engle, Kraft and Kroner (BEKK) parameterization and finds evidence of bidirectional volatility spillovers between spot and futures markets. However, there is pronounced spillover effect of a previous shock and volatility from the futures market to spot market. Hence, we conclude that Nifty futures prices lead spot prices and futures market largely contributes to price discovery. These findings have significant implications for traders in implementing hedging and arbitrage trading strategies, for portfolio managers in managing risk and also for policymakers in assessing market stability.

Suggested Citation

  • Pratap Chandra Pati & Prabina Rajib, 2011. "Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures," Applied Economics Letters, Taylor & Francis Journals, vol. 18(6), pages 567-574.
    • Handle: RePEc:taf:apeclt:v:18:y:2011:i:6:p:567-574
    DOI: 10.1080/13504851003742442
    as

    Download full text from publisher

    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504851003742442&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851003742442?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2022. "Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    2. Yi-Tsung Lee & Wei-Shao Wu & Yun Yang, 2013. "Informed Futures Trading and Price Discovery: Evidence from Taiwan Futures and Stock Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 20(3), pages 219-242, September.
    3. Jusoh, Hashim & Bacha, Obiyathulla & Masih, Abul Mansur M., 2014. "Multi-scale Lead-Lag Relationship between the Stock and Futures Markets: Malaysia as a Case Study," MPRA Paper 56954, University Library of Munich, Germany.
    4. Krauss, Christopher & Herrmann, Klaus & Teis, Stefan, 2015. "On the power and size properties of cointegration tests in the light of high-frequency stylized facts," FAU Discussion Papers in Economics 11/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Antonakakis, Nikolaos & Floros, Christos & Kizys, Renatas, 2016. "Dynamic spillover effects in futures markets: UK and US evidence," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 406-418.
    6. Mandal, Nivedita & Das, Rituparna, 2022. "Price Discovery Efficiency and Resilience of Financial Futures - A Case Study of Indian Banking Sector," MPRA Paper 112844, University Library of Munich, Germany.
    7. Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," JRFM, MDPI, vol. 10(1), pages 1-24, February.
    8. Antonakakis, Nikolaos & Kizys, Renatas & Floros, Christos, 2014. "Dynamic Spillover Effects in Futures Markets," MPRA Paper 53876, University Library of Munich, Germany.
    9. Aravind Sampath & Arun Kumar Gopalaswamy, 2020. "Intraday Variability and Trading Volume: Evidence from National Stock Exchange," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(3), pages 271-295, December.
    10. Gagan Sharma & Parthajit Kayal & Piyush Pandey, 2019. "Information Linkages Among BRICS Countries: Empirical Evidence from Implied Volatility Indices," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(3), pages 263-289, December.
    11. Sanjay Sehgal & Mala Dutt, 2016. "Domestic and international information linkages between NSE Nifty spot and futures markets: an empirical study for India," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 239-258, September.
    12. Sarveshwar Kumar Inani, 2017. "Price discovery in Indian stock index futures market: new evidence based on intraday data," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 14(1), pages 23-43.
    13. Huo, Rui & Ahmed, Abdullahi D., 2018. "Relationships between Chinese stock market and its index futures market: Evaluating the impact of QFII scheme," Research in International Business and Finance, Elsevier, vol. 44(C), pages 135-152.
    14. Gong, Chen-Chen & Ji, Shen-Dan & Su, Li-Ling & Li, Sai-Ping & Ren, Fei, 2016. "The lead–lag relationship between stock index and stock index futures: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 63-72.

    More about this item

    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:taf:apeclt:v:18:y:2011:i:6:p:567-574. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.