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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.

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Bibliographic Info

Article provided by American Finance Association in its journal Journal of Finance.

Volume (Year): 46 (1991)
Issue (Month): 4 (September)
Pages: 1251-61

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Handle: RePEc:bla:jfinan:v:46:y:1991:i:4:p:1251-61

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Cited by:
  1. Lucy F. Ackert & Marie D. Racine, 1998. "Stochastic trends and cointegration in the market for equities," Working Paper 98-13, Federal Reserve Bank of Atlanta.
  2. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
  3. Cornelis Los, 2004. "Measuring the Degree of Efficiency of Financial Market," Finance 0411003, EconWPA.
  4. Ayla Ogus, 2002. "Pricing of S&P 100 Index Options Based On Garch Volatility Estimates," Working Papers 0201, Izmir University of Economics.
  5. Rodriguez, J.C., 2007. "Option Pricing and Momentum," Discussion Paper 2007-93, Tilburg University, Center for Economic Research.
  6. 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.
  7. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
  8. Simona Sanfelici, 2007. "Calibration of a nonlinear feedback option pricing model," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 95-110.
  9. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1996. "Implied Volatility Functions: Empirical Tests," NBER Working Papers 5500, National Bureau of Economic Research, Inc.
  10. 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.
  11. George Skiadopoulos, 2004. "The Greek implied volatility index: construction and properties," Applied Financial Economics, Taylor & Francis Journals, vol. 14(16), pages 1187-1196.
  12. Michael Dueker & Thomas W. Miller, Jr., 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.
  13. 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.

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