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Option-based forecasts of volatility: an empirical study in the DAX-index options market

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  • S. Muzzioli

Abstract

Volatility estimation and forecasting are essential for both the pricing and the risk management of derivative securities. Volatility forecasting methods can be divided into option-based ones, which use prices of traded options in order to unlock volatility expectations, and time series volatility models, which use historical information in order to predict future volatility. Among option-based volatility forecasts, we distinguish between the 'model-dependent' Black-Scholes implied volatility and the 'model-free' implied volatility, proposed by Britten-Jones and Neuberger [Option prices, implied price processes and stochastic volatility. Journal of Finance 55: 839-66], that does not rely on a particular option pricing model. The aim of this paper is to investigate the unbiasedness and efficiency, with respect to past realised volatility, of the two option-based volatility forecasts. The comparison is pursued by using intra-daily data on the DAX-index options market. Our results suggest that Black-Scholes implied volatility subsumes all the information contained in past realised volatility and is a better predictor for future realised volatility than model-free implied volatility.

Suggested Citation

  • S. Muzzioli, 2010. "Option-based forecasts of volatility: an empirical study in the DAX-index options market," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 561-586.
  • Handle: RePEc:taf:eurjfi:v:16:y:2010:i:6:p:561-586
    DOI: 10.1080/13518471003640134
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    References listed on IDEAS

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    1. Busch, Thomas & Christensen, Bent Jesper & Nielsen, Morten Ørregaard, 2011. "The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets," Journal of Econometrics, Elsevier, vol. 160(1), pages 48-57, January.
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    Cited by:

    1. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.
    2. Elisabetta Gualandri & Valeria Venturelli, 2013. "The financing of Italian firms and the credit crunch: findings and exit strategies," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 13101, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    3. Imlak Shaikh & Puja Padhi, 2014. "The forecasting performance of implied volatility index: evidence from India VIX," Economic Change and Restructuring, Springer, vol. 47(4), pages 251-274, November.
    4. Muzzioli, Silvia, 2015. "The optimal corridor for implied volatility: From periods of calm to turmoil," Journal of Economics and Business, Elsevier, vol. 81(C), pages 77-94.
    5. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2015. "Towards a skewness index for the Italian stock market," Department of Economics 0064, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. Elena Giarda & Gloria Moroni, 2018. "The Degree of Poverty Persistence and the Role of Regional Disparities in Italy in Comparison with France, Spain and the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 163-202, February.
    7. C. Pederzoli & C. Torricelli, 2013. "Efficiency and unbiasedness of corn futures markets: new evidence across the financial crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 23(24), pages 1853-1863, December.
    8. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2016. "Fear or greed? What does a skewness index measure?," Department of Economics 0102, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    9. Szabolcs Blazsek & Anna Downarowicz, 2013. "Forecasting hedge fund volatility: a Markov regime-switching approach," The European Journal of Finance, Taylor & Francis Journals, vol. 19(4), pages 243-275, April.
    10. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-015-2079-y is not listed on IDEAS
    11. Silvia Muzzioli, 2011. "Corridor implied volatility and the variance risk premium in the Italian market," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 11112, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    12. Silvia Muzzioli, 2013. "The Optimal Corridor for Implied Volatility: from Calm to Turmoil Periods," Department of Economics (DEMB) 0029, University of Modena and Reggio Emilia, Department of Economics "Marco Biagi".
    13. Stefano Cosma & Elisabetta Gualandri, 2014. "The sovereign debt crisis: the impact on the intermediation model of Italian banks," BANCARIA, Bancaria Editrice, vol. 2, pages 48-60, February.
    14. Elisabetta Gualandri & Mario Noera, 2014. "Towards A Macroprudential Policy In The Eu: Main Issues," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 14110, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    15. Kearney, Fearghal & Murphy, Finbarr & Cummins, Mark, 2015. "An analysis of implied volatility jump dynamics: Novel functional data representation in crude oil markets," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 199-216.
    16. Carlo Alberto Magni, 2015. "Pseudo-naïve approaches to investment performance measurement," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 15021, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    17. Elyas Elyasiani & Silvia Muzzioli & Alessio Ruggieri, 2016. "Forecasting and pricing powers of option-implied tree models: Tranquil and volatile market conditions," Department of Economics 0099, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    18. Silvia Muzzioli, 2013. "The Forecasting Performance of Corridor Implied Volatility in the Italian Market," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 359-386, March.
    19. Elisabetta Gualandri & Mario Noera, 2014. "Monitoring Systemic Risk: A Survey Of The Available Macroprudential Toolkit," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 14111, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    20. Enrico Rubaltelli & Sergio Agnoli & Michela Rancan & Tiziana Pozzoli, 2015. "Emotional Intelligence and risk taking in investment decision-making," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 15107, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    21. Chiara Pederzoli & Costanza Torricelli, 2010. "A parsimonious default prediction model for Italian SMEs," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 10061, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

    More about this item

    Keywords

    Black-Scholes implied volatility; model-free implied volatility; volatility forecasting;

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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