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The Information Content of Alternate Implied Volatility Models: Case of Indian Markets

Author

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  • A. Vinay Kumar

    (A. Vinay Kumar (corresponding author), Associate Professor, Finance and Accounting Group, Indian Institute of Management, Prabandh Nagar, Off Sitapur Road, Lucknow, India. E-mail: vinay@iiml.ac.in)

  • Shikha Jaiswal

    (Shikha Jaiswal, doctoral student of Finance, Emory University, USA. E-mail: shikhaj25@gmail.com)

Abstract

The information content to forecast the future realised volatility by model-based implied volatility and model free implied volatility has been an increasingly important area of research. There is a lot of literature suggesting that Black Scholes implied volatility is an efficient estimator ( Fleming 1998 ); on the contrary there are studies suggesting that model free measures like VIX could be more informative ( Jiang and Tian 2005 ). There is also a view, as evidenced in the earlier research, that there is a complete lack of relationship between them. The current study seeks to understand these dynamics in the Indian context and add to the debate by specifying market direction variables which could explain the relationship. The study tries to bring out the short-term relationship between the two volatilities. The present study finds evidence supporting Fleming ( 1998 ) and contrary to the evidence suggested by Jiang and Tian ( 2005 ), that is, Black Scholes implied volatility dominates the forecasting efficiency over the VIX even though both estimates are biased. JEL Classification: G12, G13, G14, C22

Suggested Citation

  • A. Vinay Kumar & Shikha Jaiswal, 2013. "The Information Content of Alternate Implied Volatility Models: Case of Indian Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 12(3), pages 293-321, December.
    • Handle: RePEc:sae:emffin:v:12:y:2013:i:3:p:293-321
    DOI: 10.1177/0972652713512915
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    References listed on IDEAS

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    1. Mark Britten‐Jones & Anthony Neuberger, 2000. "Option Prices, Implied Price Processes, and Stochastic Volatility," Journal of Finance, American Finance Association, vol. 55(2), pages 839-866, April.
    2. Sankarshan Basu & Bappaditya Mukhopadhyay, 2006. "Derivatives in Asia-Pacific Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(3), pages 207-215, December.
    3. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    4. Amin, Kaushik I & Ng, Victor K, 1997. "Inferring Future Volatility from the Information in Implied Volatility in Eurodollar Options: A New Approach," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 333-367.
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    Cited by:

    1. Alok Dixit & Shivam Singh, 2018. "Ad-Hoc Black–Scholes vis-à-vis TSRV-based Black–Scholes: Evidence from Indian Options Market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(1), pages 57-88, March.

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    More about this item

    Keywords

    Black Scholes implied volatility; VIX; realised volatility; historical volatility; market efficiency; generalised method of movements;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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