IDEAS home Printed from https://ideas.repec.org/p/mod/wcefin/10091.html
   My bibliography  Save this paper

Towards a volatility index for the Italian stock market

Author

Listed:
  • Silvia Muzzioli

Abstract

The aim of this paper is to analyse and empirically test how to unlock volatility information from option prices. The information content of three option based forecasts of volatility: Black-Scholes implied volatility, model-free implied volatility and corridor implied volatility is addressed, with the ultimate plan of proposing a new volatility index for the Italian stock market. As for model-free implied volatility, two different extrapolation techniques are implemented. As for corridor implied volatility, five different corridors are compared. Our results, which point to a better performance of corridor implied volatilities with respect to both Black-Scholes implied volatility and model-free implied volatility, are in favour of narrow corridors. The volatility index proposed is obtained with an overall 50% cut of the risk neutral distribution. The properties of the volatility index are explored by analysing both the contemporaneous relationship between implied volatility changes and market returns and the usefulness of the proposed index in forecasting future market returns.

Suggested Citation

  • Silvia Muzzioli, 2010. "Towards a volatility index for the Italian stock market," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 10091, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  • Handle: RePEc:mod:wcefin:10091
    as

    Download full text from publisher

    File URL: http://155.185.68.2/CefinPaper/CEFIN-WP23.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    3. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    4. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    5. Tsiaras, Leonidas, 2009. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," Finance Research Group Working Papers F-2009-02, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    6. Bondarenko, Oleg, 2003. "Estimation of risk-neutral densities using positive convolution approximation," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 85-112.
    7. Melick, William R. & Thomas, Charles P., 1997. "Recovering an Asset's Implied PDF from Option Prices: An Application to Crude Oil during the Gulf Crisis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(1), pages 91-115, March.
    8. Mark Rubinstein., 1994. "Implied Binomial Trees," Research Program in Finance Working Papers RPF-232, University of California at Berkeley.
    9. Tim Bollerslev & George Tauchen & Hao Zhou, 2009. "Expected Stock Returns and Variance Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4463-4492, November.
    10. Emanuel Derman & Iraj Kani, 1998. "Stochastic Implied Trees: Arbitrage Pricing with Stochastic Term and Strike Structure of Volatility," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 61-110.
    11. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
    14. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    15. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    16. Rubinstein, Mark, 1994. "Implied Binomial Trees," Journal of Finance, American Finance Association, vol. 49(3), pages 771-818, July.
    17. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    18. Jackwerth, Jens Carsten, 1996. "Generalized Binomial Trees," MPRA Paper 11635, University Library of Munich, Germany, revised 12 May 1997.
    19. Stutzer, Michael, 1996. "A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-1652, December.
    20. Torben G. Andersen & Oleg Bondarenko, 2007. "Construction and Interpretation of Model-Free Implied Volatility," CREATES Research Papers 2007-24, Department of Economics and Business Economics, Aarhus University.
    21. George Skiadopoulos, 2004. "The Greek implied volatility index: construction and properties," Applied Financial Economics, Taylor & Francis Journals, vol. 14(16), pages 1187-1196.
    22. Moriggia, V. & Muzzioli, S. & Torricelli, C., 2009. "On the no-arbitrage condition in option implied trees," European Journal of Operational Research, Elsevier, vol. 193(1), pages 212-221, February.
    23. Monteiro, Ana Margarida & Tutuncu, Reha H. & Vicente, Luis N., 2008. "Recovering risk-neutral probability density functions from options prices using cubic splines and ensuring nonnegativity," European Journal of Operational Research, Elsevier, vol. 187(2), pages 525-542, June.
    24. Campa, Jose M. & Chang, P. H. Kevin & Reider, Robert L., 1998. "Implied exchange rate distributions: evidence from OTC option markets1," Journal of International Money and Finance, Elsevier, vol. 17(1), pages 117-160, February.
    25. 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.
    26. Jackwerth, Jens Carsten, 1999. "Option Implied Risk-Neutral Distributions and Implied Binomial Trees: A Literature Review," MPRA Paper 11634, University Library of Munich, Germany.
    27. GIOT, Pierre, 2005. "Implied volatility indexes and daily Value at Risk models," LIDAM Reprints CORE 1840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    28. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    29. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Dean Altshuler & Carlo Alberto Magni, 2015. "Introducing Aggregate Return on Investment as a Solution to the Contradiction Between Some PME Metrics and IRR," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0056, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    2. Marianna Brunetti & Roberta de Luca, 2022. "Pre-selection in cointegration-based pairs trading," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0089, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    3. 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) 0030, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silvia Muzzioli, 2013. "The Information Content of Option-Based Forecasts of Volatility: Evidence from the Italian Stock Market," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 1-46.
    2. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    3. 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.
    4. 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) 0030, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    5. 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".
    6. 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".
    7. Tsiaras, Leonidas, 2009. "The Forecast Performance of Competing Implied Volatility Measures: The Case of Individual Stocks," Finance Research Group Working Papers F-2009-02, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    8. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    9. 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".
    10. 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.
    11. Monteiro, Ana Margarida & Tutuncu, Reha H. & Vicente, Luis N., 2008. "Recovering risk-neutral probability density functions from options prices using cubic splines and ensuring nonnegativity," European Journal of Operational Research, Elsevier, vol. 187(2), pages 525-542, June.
    12. Chen, Ren-Raw & Hsieh, Pei-lin & Huang, Jeffrey, 2018. "Crash risk and risk neutral densities," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 162-189.
    13. Chris Charalambous & Nicos Christofides & Eleni D. Constantinide & Spiros H. Martzoukos, 2007. "Implied non-recombining trees and calibration for the volatility smile," Quantitative Finance, Taylor & Francis Journals, vol. 7(4), pages 459-472.
    14. Bugge, Sebastian A. & Guttormsen, Haakon J. & Molnár, Peter & Ringdal, Martin, 2016. "Implied volatility index for the Norwegian equity market," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 133-141.
    15. Coutant, Sophie & Jondeau, Eric & Rockinger, Michael, 2001. "Reading PIBOR futures options smiles: The 1997 snap election," Journal of Banking & Finance, Elsevier, vol. 25(11), pages 1957-1987, November.
    16. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    17. Bondarenko, Oleg, 2003. "Estimation of risk-neutral densities using positive convolution approximation," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 85-112.
    18. Wael Bahsoun & Pawel Góra & Silvia Mayoral & Manuel Morales, 2006. "Random Dynamics and Finance: Constructing Implied Binomial Trees from a Predetermined Stationary Den," Faculty Working Papers 13/06, School of Economics and Business Administration, University of Navarra.
    19. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    20. Siriopoulos, Costas & Fassas, Athanasios, 2012. "An investor sentiment barometer — Greek Implied Volatility Index (GRIV)," Global Finance Journal, Elsevier, vol. 23(2), pages 77-93.

    More about this item

    Keywords

    volatility index; Black-Scholes implied volatility; model-free implied volatility; corridor implied volatility; implied binomial trees;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mod:wcefin:10091. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Giuseppe Marotta (email available below). General contact details of provider: https://edirc.repec.org/data/demodit.html .

    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.