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The optimal corridor for implied volatility: From periods of calm to turmoil

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  • Muzzioli, Silvia

Abstract

Corridor implied volatility is obtained from model-free implied volatility by truncating the integration domain between two barriers. Empirical evidence on volatility forecasting in various markets points to the utility of trimming the risk-neutral distribution of the underlying stock price, in order to obtain unbiased measures of future realized volatility. The aim of this paper is to investigate the optimal corridor of strike prices for volatility forecasting in the Italian market, by analyzing numerous symmetric and asymmetric corridors in a dataset for the years 2005–2010 spanning both a relatively calm period and a period of turmoil. The results indicate that put prices, providing information on the probability of a downturn of the underlying asset, provide the best indication of future realized volatility, particularly in a period of turmoil.

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  • 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.
    • Handle: RePEc:eee:jebusi:v:81:y:2015:i:c:p:77-94
    DOI: 10.1016/j.jeconbus.2015.07.001
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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. 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.
    3. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
    4. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    5. Amadeo Alentorn & Sheri Markose, 2008. "Generalized Extreme Value Distribution and Extreme Economic Value at Risk (EE-VaR)," Springer Books, in: Erricos J. Kontoghiorghes & Berç Rustem & Peter Winker (ed.), Computational Methods in Financial Engineering, pages 47-71, Springer.
    6. Turan G. Bali & Armen Hovakimian, 2009. "Volatility Spreads and Expected Stock Returns," Management Science, INFORMS, vol. 55(11), pages 1797-1812, November.
    7. Adrian Buss & Grigory Vilkov, 2012. "Measuring Equity Risk with Option-implied Correlations," The Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3113-3140.
    8. Bruno, Valentina & Shin, Hyun Song, 2015. "Capital flows and the risk-taking channel of monetary policy," Journal of Monetary Economics, Elsevier, vol. 71(C), pages 119-132.
    9. Bernard Dumas & Alexander Kurshev & Raman Uppal, 2009. "Equilibrium Portfolio Strategies in the Presence of Sentiment Risk and Excess Volatility," Journal of Finance, American Finance Association, vol. 64(2), pages 579-629, April.
    10. 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.
    11. Joshua D. Coval & Tyler Shumway, 2001. "Expected Option Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 983-1009, June.
    12. Lawrence J. Christiano & Roberto Motto & Massimo Rostagno, 2014. "Risk Shocks," American Economic Review, American Economic Association, vol. 104(1), pages 27-65, January.
    13. Erricos J. Kontoghiorghes & Berç Rustem & Peter Winker (ed.), 2008. "Computational Methods in Financial Engineering," Springer Books, Springer, number 978-3-540-77958-2, September.
    14. repec:pri:cepsud:237b%20shin is not listed on IDEAS
    15. 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.
    16. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    17. Alexandros Kostakis & Nikolaos Panigirtzoglou & George Skiadopoulos, 2011. "Market Timing with Option-Implied Distributions: A Forward-Looking Approach," Management Science, INFORMS, vol. 57(7), pages 1231-1249, July.
    18. 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.
    19. Bruno, Valentina & Shin, Hyun Song, 2015. "Capital flows and the risk-taking channel of monetary policy," Journal of Monetary Economics, Elsevier, vol. 71(C), pages 119-132.
    20. DeMiguel, Victor & Plyakha, Yuliya & Uppal, Raman & Vilkov, Grigory, 2013. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1813-1845, December.
    21. 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.
    22. Chris Woolston, 2014. "Rice," Nature, Nature, vol. 514(7524), pages 49-49, October.
    23. 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.
    24. Cremers, Martijn & Weinbaum, David, 2010. "Deviations from Put-Call Parity and Stock Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 335-367, April.
    25. 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.
    26. 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).
    27. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    28. 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.
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    Citations

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    Cited by:

    1. 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.
    2. Elyas Elyasani & Luca Gambarelli & Silvia Muzzioli, 2016. "The risk asymmetry index," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0061, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    3. 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".
    4. Elyas Elyasani & Luca Gambarelli & Silvia Muzzioli, 2016. "The risk asymmetry index," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 16212, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    5. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2018. "The use of option prices in order to evaluate the skewness risk premium," Department of Economics 0132, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. 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".
    7. 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".
    8. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2018. "The properties of a skewness index and its relation with volatility and returns," Department of Economics 0133, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".

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    More about this item

    Keywords

    Corridor implied volatility; Model-free implied volatility; Volatility forecasting; Financial turmoil;
    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

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