The relationship between implied and realized volatility of S&P 500 index

This paper studies the relation between implied and realized volatility by using daily S&P 500 index option price over the period between January 1995 and December 1999. In particular, we want to test the how different measurement errors affect the stability of this relationship. Two sources of measurement errors are considered. The first one is the measurement error in realized volatility. Four different estimators of computing realized volatility are tested. They are the standard deviation of daily return; the Parkinson (1980) extreme value volatility estimator, the Yang & Zhang (2000) range estimator, and the square root of intraday return squares (Andersen, 2000). The second source of error comes from model specification. The implied volatility computed from Black Scholes model is compared with that from calibrated Heston (1993) stochastic volatility optionpricing model. We find the improvement of the measurement of realized volatility can significantly improve the forecast ability of implied volatility, with the realized volatility estimated from intraday return data is most predictable. However, there is no significant difference in forecasting realized volatility using implied volatility either from BlackScholes model or from Heston model. When both implied volatility and historical volatility are used to forecast realized volatility, we find implied volatility outperforms historical volatility and even subsumes information of historical volatility. This result h olds for all measurements of realized volatility and implied volatility.

[1]  M. Rubinstein. Nonparametric tests of alternative option pricing models using all reported trades and quotes on the , 1985 .

[2]  M. Parkinson The Extreme Value Method for Estimating the Variance of the Rate of Return , 1980 .

[3]  Alan G. White,et al.  The Pricing of Options on Assets with Stochastic Volatilities , 1987 .

[4]  Stephen Figlewski,et al.  The Informational Content of Implied Volatility , 1993 .

[5]  S. Heston A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options , 1993 .

[6]  Jeff Fleming,et al.  The Value of Wildcard Options , 1994 .

[7]  Jeff Fleming,et al.  Implied volatility functions: empirical tests , 1996, IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[8]  Stephen L Taylor,et al.  The incremental volatility information in one million foreign exchange quotations , 1997 .

[9]  Gurdip Bakshi,et al.  Empirical Performance of Alternative Option Pricing Models , 1997 .

[10]  Saikat Nandi,et al.  How important is the correlation between returns and volatility in a stochastic volatility model? Empirical evidence from pricing and hedging in the S&P 500 index options market , 1998 .

[11]  N. Prabhala,et al.  The relation between implied and realized volatility , 1998 .

[12]  B. Dumas,et al.  Implied volatility functions: empirical tests , 1996, IEEE Conference on Computational Intelligence for Financial Engineering & Economics.

[13]  Jeff Fleming The quality of market volatility forecasts implied by S&P 100 index option prices , 1998 .

[14]  D. Yang,et al.  Drift Independent Volatility Estimation Based on High, Low, Open, and Close Prices , 2000 .

[15]  T. Andersen Some Reflections on Analysis of High-Frequency Data , 2000 .