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S&P 100 Index Option Volatility

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  • Harvey, Campbell R
  • Whaley, Robert E

Abstract

Using transaction data on the S&P 100 index options, the authors study the effect of valuation simplifications that are commonplace in previous research on the time-series properties of implied market volatility. Using an American-style algorithm that accounts for the discrete nature of the dividends on the S&P 100 index, they find that spurious negative serial correlation in implied volatility changes is induced by nonsimultaneously observing the option price and the index level. Negative serial correlation is also induced by a bid/ask price effect if a single option is used to estimate implied volatility. In addition, the authors find that these same effects induce spurious (and unreasonable) negative cross-correlations between the changes in call and put implied volatility. Copyright 1991 by American Finance Association. See http://www.jstor.org for details.

Suggested Citation

  • Harvey, Campbell R & Whaley, Robert E, 1991. " S&P 100 Index Option Volatility," Journal of Finance, American Finance Association, vol. 46(4), pages 1251-1261, September.
  • Handle: RePEc:bla:jfinan:v:46:y:1991:i:4:p:1251-61
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    Citations

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    Cited by:

    1. Simona Sanfelici, 2007. "Calibration of a nonlinear feedback option pricing model," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 95-110.
    2. Jamdee, Sutthisit & Los, Cornelis A., 2007. "Long memory options: LM evidence and simulations," Research in International Business and Finance, Elsevier, vol. 21(2), pages 260-280, June.
    3. Tomas Havranek & Anna Sokolova, 2016. "Do Consumers Really Follow a Rule of Thumb? Three Thousand Estimates from 130 Studies Say "Probably Not"," Working Papers 2016/08, Czech National Bank, Research Department.
    4. Ayla Ogus, 2002. "Pricing of S&P 100 Index Options Based On Garch Volatility Estimates," Working Papers 0201, Izmir University of Economics.
    5. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
    6. Alok Kumar Mishra & Siba Prasad Panda, 2016. "Looking into the relationship between implied and realized volatility: a study on S&P CNX Nifty index option," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(1), pages 67-96, April.
    7. Ackert, Lucy F. & Racine, M. D., 1999. "Stochastic trends and cointegration in the market for equities," Journal of Economics and Business, Elsevier, vol. 51(2), pages 133-143, March.
    8. Bent Jesper Christensen & Charlotte Strunk Hansen, 2002. "New evidence on the implied-realized volatility relation," The European Journal of Finance, Taylor & Francis Journals, vol. 8(2), pages 187-205, June.
    9. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    10. George Skiadopoulos, 2004. "The Greek implied volatility index: construction and properties," Applied Financial Economics, Taylor & Francis Journals, vol. 14(16), pages 1187-1196.
    11. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1996. "Implied Volatility Functions: Empirical Tests," NBER Working Papers 5500, National Bureau of Economic Research, Inc.
    12. Panigirtzoglou, Nikolaos & Skiadopoulos, George, 2004. "A new approach to modeling the dynamics of implied distributions: Theory and evidence from the S&P 500 options," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1499-1520, July.
    13. Cornelis Los, 2004. "Measuring the Degree of Efficiency of Financial Market," Finance 0411003, EconWPA.
    14. Michael J. Dueker & Thomas W. Miller, 1996. "Market microstructure effects on the direct measurement of the early exercise premium in exchange-listed options," Working Papers 1996-013, Federal Reserve Bank of St. Louis.

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