Wiggins, J.G., "Estimating the Volatility of S&P 500 Futures Prices Using the Extreme-Value Metho d." The Journal of Futures Markets, Vol. 12 No. 3 (June 1992), pp. 265-273. Home/Search Document Not in Database Summary Related Articles Check |
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Information Dispersal: A Microstructure Analysis of Stock.. - Daigler, Herbst (Correct)
....days are missing from the database, the market closed early after the October 1987 crash (which carried into November 1987) and the number of trading days varies across months. The first 15 minutes of the MMI contract, which trades before the cash markets open, is omitted from the data. 14 Wiggins (1992) shows that the Garman Klass estimator is only slightly downwardly biased, and is significantly more efficient than using close to close data. 7 Hypothesis 3: Insignificant coherences support the independent markets theory. III. Data The five most volatile months since the initiation of ....
Wiggins, J.G., "Estimating the Volatility of S&P 500 Futures Prices Using the Extreme-Value Metho d." The Journal of Futures Markets, Vol. 12 No. 3 (June 1992), pp. 265-273.
The Quality of Market Volatility Forecasts Implied by S&P 100.. - Fleming (1997) (9 citations) (Correct)
....and therefore the slope coefficient should be less than one. 15 For a stock index portfolio, the Parkinson estimatorwould be a biased volatilitymeasure because infrequent trading misrepresents the true extremevalues. Index futures prices, however, are not influenced byinfrequent trading, and Wiggins (1992) illustrates the consistency of the Parkinson estimator in this context. 16 In general, the intercept equals Y 0 fi X where X and Y;respectively,are the averages of the independentand dependentvariables. For the implied volatility first differences in equation (16) X 0:0001: As a ....
Wiggins, J., 1992, Estimating the volatility of S&P 500 futures prices using the extremevalue method, Journal of Futures Markets 12, 265--273.
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