IDEAS home Printed from https://ideas.repec.org/p/aah/create/2011-49.html
   My bibliography  Save this paper

Coherent Model-Free Implied Volatility: A Corridor Fix for High-Frequency VIX

Author

Listed:
  • Torben G. Andersen

    (Kellogg School of Management; Northwestern University and CREATES)

  • Oleg Bondarenko

    (Department of Finance (MC 168), University of Illinois at Chicago)

  • Maria T. Gonzalez-Perez

    (Colegio Universitario de Estudios Financieros (CUNEF))

Abstract

The VIX index is computed as a weighted average of SPX option prices over a range of strikes according to specific rules regarding market liquidity. It is explicitly designed to provide a model-free option-implied volatility measure. Using tick-by-tick observations on the underlying options, we document a substantial time variation in the coverage which the stipulated strike range affords for the distribution of future S&P 500 index prices. This produces idiosyncratic biases in the measure, distorting the time series properties of VIX. We introduce a novel “Corridor Implied Volatility” index (CX) computed from a strike range covering an “economically invariant” proportion of the future S&P 500 index values. We find the CX measure superior in filtering out noise and eliminating artificial jumps, thus providing a markedly different characterization of the high-frequency volatility dynamics. Moreover, the VIX measure is particularly unreliable during periods of market stress, exactly when a “fear gauge” is most valuable.

Suggested Citation

  • Torben G. Andersen & Oleg Bondarenko & Maria T. Gonzalez-Perez, 2011. "Coherent Model-Free Implied Volatility: A Corridor Fix for High-Frequency VIX," CREATES Research Papers 2011-49, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2011-49
    as

    Download full text from publisher

    File URL: https://repec.econ.au.dk/repec/creates/rp/11/rp11_49.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. Todorov, Viktor & Tauchen, George, 2010. "Activity signature functions for high-frequency data analysis," Journal of Econometrics, Elsevier, vol. 154(2), pages 125-138, February.
    3. Torben G. Andersen & Oleg Bondarenko, 2007. "Construction and Interpretation of Model-Free Implied Volatility," NBER Working Papers 13449, National Bureau of Economic Research, Inc.
    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. Andersen, Torben G. & Bondarenko, Oleg, 2014. "VPIN and the flash crash," Journal of Financial Markets, Elsevier, vol. 17(C), pages 1-46.
    2. Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2015. "Parametric Inference and Dynamic State Recovery From Option Panels," Econometrica, Econometric Society, vol. 83(3), pages 1081-1145, May.
    3. 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.
    4. repec:esx:essedp:713 is not listed on IDEAS
    5. Takkabutr, Nattapol, 2013. "Option-Implied Risk Aversion Anomalies: Evidence From Japanese Market," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 54(2), pages 137-157, December.
    6. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    7. Shan Lu, 2019. "Testing the Predictive Ability of Corridor Implied Volatility Under GARCH Models," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(2), pages 129-168, June.
    8. Konstantinidi, Eirini & Skiadopoulos, George, 2016. "How does the market variance risk premium vary over time? Evidence from S&P 500 variance swap investment returns," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 62-75.
    9. Markose, Sheri M & Peng, Yue & Alentorn, Amadeo, 2012. "Forecasting Extreme Volatility of FTSE-100 With Model Free VFTSE, Carr-Wu and Generalized Extreme Value (GEV) Option Implied Volatility Indices," Economics Discussion Papers 3713, University of Essex, Department of Economics.
    10. Barunik, Jozef & Barunikova, Michaela, 2015. "Revisiting the long memory dynamics of implied-realized volatility relation: A new evidence from wavelet band spectrum regression," FinMaP-Working Papers 43, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    11. Jozef Barunik & Michaela Barunikova, 2012. "Revisiting the fractional cointegrating dynamics of implied-realized volatility relation with wavelet band spectrum regression," Papers 1208.4831, arXiv.org, revised Feb 2013.
    12. Bondarenko, Oleg, 2014. "Variance trading and market price of variance risk," Journal of Econometrics, Elsevier, vol. 180(1), pages 81-97.
    13. Rohini Grover & Ajay Shah, 2014. "The imprecision of volatility indexes," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2014-031, Indira Gandhi Institute of Development Research, Mumbai, India.
    14. Kent Daniel & Ravi Jagannathan & Soohun Kim, 2012. "Tail Risk in Momentum Strategy Returns," NBER Working Papers 18169, National Bureau of Economic Research, Inc.

    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. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    7. Xingzhi Yao & Marwan Izzeldin, 2018. "Forecasting using alternative measures of model‐free option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 199-218, February.
    8. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2018. "The Risk-Asymmetry Index as a new Measure of Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 22(3-4), pages 173-210, September.
    9. Shan Lu, 2019. "Testing the Predictive Ability of Corridor Implied Volatility Under GARCH Models," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(2), pages 129-168, June.
    10. Maria Gonzalez-Perez & Alfonso Novales, 2011. "The information content in a volatility index for Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(2), pages 185-216, June.
    11. Birkelund, Ole Henrik & Haugom, Erik & Molnár, Peter & Opdal, Martin & Westgaard, Sjur, 2015. "A comparison of implied and realized volatility in the Nordic power forward market," Energy Economics, Elsevier, vol. 48(C), pages 288-294.
    12. Bondarenko, Oleg, 2014. "Variance trading and market price of variance risk," Journal of Econometrics, Elsevier, vol. 180(1), pages 81-97.
    13. Kräussl, Roman & Oladiran, Tobi & Stefanova, Denitsa, 2023. "ESG as protection against downside risk," CFS Working Paper Series 708, Center for Financial Studies (CFS).
    14. 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".
    15. Jianlei Han & Martina Linnenluecke & Zhangxin Liu & Zheyao Pan & Tom Smith, 2019. "A general equilibrium approach to pricing volatility risk," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-18, April.
    16. 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".
    17. repec:esx:essedp:713 is not listed on IDEAS
    18. Söylemez, Arif Orçun, 2020. "How Do Volatility and Return Series Interact?," MPRA Paper 104687, University Library of Munich, Germany.
    19. Tzang, Shyh-Weir & Hung, Chih-Hsing & Wang, Chou-Wen & Shyu, David So-De, 2011. "Do liquidity and sampling methods matter in constructing volatility indices? Empirical evidence from Taiwan," International Review of Economics & Finance, Elsevier, vol. 20(2), pages 312-324, April.
    20. Luca Gambarelli & Silvia Muzzioli, 2019. "Risk-asymmetry indices in Europe," Department of Economics 0157, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    21. Kian-Guan Lim & Christopher Ting, 2012. "The term structure of S&P 100 model-free volatilities," Quantitative Finance, Taylor & Francis Journals, vol. 13(7), pages 1041-1058, November.

    More about this item

    Keywords

    VIX; Model-Free Implied Volatility; Corridor Implied Volatility; Time Series Coherence;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:aah:create:2011-49. 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: the person in charge (email available below). General contact details of provider: http://www.econ.au.dk/afn/ .

    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.