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An Empirical Test of Market Efficiency of Indian Index Options Market Using the Black–Scholes Model and Dynamic Hedging Strategy

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  • Debaditya Mohanti
  • P. K. Priyan

Abstract

The purpose of the present study is to provide evidence on the market efficiency of the S&P CNX Nifty index options traded on the National Stock Exchange (NSE) of India, using the Black–Scholes model and dynamic hedging strategy. Till today, a limited literature exists on the market efficiency of the Indian index option by using a dynamic hedging strategy. Thus, an effort has been made in constructing the dynamic hedging strategy to examine whether the violations obtained from the Black–Scholes model can be covered by the costs in setting up the dynamic hedge strategy. The data collected in this study consist of daily closing prices of S&P CNX Nifty index options contracts from 1 April 2008 to 31 March 2012. The results demonstrate that, although the sensitivity analysis of violations with respect to liquidity, time to maturity and moneyness show that the majority of violations in options are exploitable, the proportion of exploitable violations severely falls after considering the transaction cost, as most of the profits were wiped out and showing negative profits. Thus, although the Indian index options market shows traces of inefficiency, in totality, it is suggested that the Indian index options market is efficient as a majority of violations are un-exploitable after incorporating transaction cost.

Suggested Citation

  • Debaditya Mohanti & P. K. Priyan, 2014. "An Empirical Test of Market Efficiency of Indian Index Options Market Using the Black–Scholes Model and Dynamic Hedging Strategy," Paradigm, , vol. 18(2), pages 221-237, December.
  • Handle: RePEc:sae:padigm:v:18:y:2014:i:2:p:221-237
    DOI: 10.1177/0971890714558709
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    References listed on IDEAS

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    Cited by:

    1. Anubha Srivastava & Manjula Shastri, 2020. "A Study of Black–Scholes Model’s Applicability in Indian Capital Markets," Paradigm, , vol. 24(1), pages 73-92, June.

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