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Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility


  • Wallmeier, Martin


We present a new method to measure the intraday relationship between movements of implied volatility smiles and stock returns. It is based on an enhanced smile regression model which captures patterns in the intraday data which have not yet been reported in the literature. Using transaction data for exchange-traded EuroStoxx 50 options from 2000 to 2011 and DAX options from 1995 to 2011, we show that, on average, about 99% of the intraday variation of implied volatility can be explained by moneyness and changes in the index level. Compared to the typical smile regression with moneyness alone, about 50% of the remaining errors can be attributed to movements in the underlying index. We find that the intraday evolution of volatility smiles is generally not consistent with traders' rules of thumb such as the sticky strike or sticky delta rule. On average, the impact of index return on implied volatility is 1.3 to 1.5 times stronger than the sticky strike rule predicts. The main factor driving variations of this adjustment factor is the index return. Our results have implications for option valuation, hedging and the understanding of the leverage effect.

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  • Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  • Handle: RePEc:fri:fribow:fribow00427

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    References listed on IDEAS

    1. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Rama Cont & Jose da Fonseca, 2002. "Dynamics of implied volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 2(1), pages 45-60.
    3. Toby Daglish & John Hull & Wulin Suo, 2007. "Volatility surfaces: theory, rules of thumb, and empirical evidence," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 507-524.
    4. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    5. Peter Christoffersen & Steven Heston & Kris Jacobs, 2009. "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work So Well," Management Science, INFORMS, vol. 55(12), pages 1914-1932, December.
    6. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1998. "Implied Volatility Functions: Empirical Tests," Journal of Finance, American Finance Association, vol. 53(6), pages 2059-2106, December.
    7. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
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    More about this item


    volatility smile; implied volatility; leverage effect; index options; highfrequency data;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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