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An Empirical Verification of Cointegration and Causality in Indian Stock Markets

Author

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  • P.R. Madhusoodanan

    (P.R. Madhusoodanan (corresponding author) is Manager, Research and Planning Division, Export–Import Bank of India, Mumbai. Email: madhusoodan_pr@yahoo.co.in)

  • Hareesh V. Kumar

    (Hareesh V. Kumar is Lecturer, Department of Management Studies, School of Management, Central University of Pondicherry. Email: skhkvk@yahoo.co.in)

Abstract

The study examines the empirical validity of cointegration and causality between the two dominating Indian stock markets: the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The daily closing values of the BSE Sensex and the NSE Nifty indices are retrieved from the PROWESS online database maintained by the Centre for Monitoring Indian Economy (CMIE). The study covers a period of nine years spanning from 1 January 1997 to 31 December 2005. The analysis reveals the existence of market integration between the BSE and the NSE. This demonstrates information dissemination between these two markets. The study further confirms the role of NSE as a dominating factor over BSE.

Suggested Citation

  • P.R. Madhusoodanan & Hareesh V. Kumar, 2008. "An Empirical Verification of Cointegration and Causality in Indian Stock Markets," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 9(1), pages 159-172, June.
    • Handle: RePEc:sae:soueco:v:9:y:2008:i:1:p:159-172
    DOI: 10.1177/139156140700900107
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    References listed on IDEAS

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    More about this item

    Keywords

    JEL: G14; JEL: C32; Johansen's Cointegration; Market Efficiency; Market Integration; Stock Market; Vector Error Correction Model;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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