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Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns. (English) Zbl 0980.62097

Summary: The information content of implied volatilities and intraday returns is compared, in the context of forecasting index volatility over horizons from 1 to 20 days. Forecasts of two measures of realised volatility are obtained after estimating ARCH models using daily index returns, daily observations of the VIX index of implied volatility and sums of squares of 5-min index returns. The in-sample estimates show that nearly all relevant information is provided by the VIX index and hence there is not much incremental information in high-frequency index returns. For out-of-sample forecasting, the VIX index provides the most accurate forecasts for all forecast horizons and performance measures considered. The evidence for incremental forecasting information in intraday returns is insignificant.

MSC:

62P05 Applications of statistics to actuarial sciences and financial mathematics
91B84 Economic time series analysis
62M20 Inference from stochastic processes and prediction
91B28 Finance etc. (MSC2000)
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[1] Akgiray, V., Conditional heteroskedasticity in time series of stock returns: evidence and forecasts, Journal of Business, 62, 55-80 (1989)
[2] Andersen, T. G.; Bollerslev, T., Answering the skeptics: yes standard volatility models do provide accurate forecasts, International Economic Review, 39, 885-905 (1998)
[3] Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; Ebens, H., The distribution of realized stock return volatility, Journal of Financial Economics, 61, 43-76 (2001)
[4] Andersen, T. G.; Bollerslev, T.; Diebold, F. X.; Labys, P., The distribution of exchange rate volatility, Journal of the American Statistical Association, 96, 42-55 (2001) · Zbl 1015.62107
[5] Bera, A. K.; Higgins, M. L., ARCH models: properties, estimation and testing, Journal of Economic Surveys, 7, 305-362 (1993)
[7] Blair, B. J.; Poon, S. H.; Taylor, S. J., Modelling S&P 100 volatility: the information content of stock returns, Journal of Banking and Finance, 25, 1665-1679 (2001)
[8] Bollerslev, T., A conditional heteroskedastic time series model for speculative prices and rates of returns, Review of Economics and Statistics, 69, 542-547 (1987)
[9] Bollerslev, T.; Chou, R. Y.; Kroner, K. P., ARCH modeling in finance: a review of theory and empirical evidence, Journal of Econometrics, 52, 5-59 (1992) · Zbl 0825.90057
[11] Bollerslev, T.; Wooldridge, J. M., Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances, Econometric Reviews, 11, 143-179 (1992) · Zbl 0850.62884
[12] Brailsford, T. J.; Faff, R. W., An evaluation of volatility forecasting techniques, Journal of Banking and Finance, 20, 419-438 (1996)
[13] Canina, L.; Figlewski, S., The informational content of implied volatility, Review of Financial Studies, 6, 659-681 (1993)
[14] Chiras, D. P.; Manaster, S., The information content of option prices and a test for market efficiency, Journal of Financial Economics, 6, 213-234 (1978)
[15] Christensen, B. J.; Prabhala, N. R., The relation between implied and realized volatility, Journal of Financial Economics, 50, 125-150 (1998)
[16] Day, T. E.; Lewis, C. M., Stock market volatility and the informational content of stock index options, Journal of Econometrics, 52, 267-287 (1992)
[17] Dimson, E.; Marsh, P., Volatility forecasting without data-snooping, Journal of Banking and Finance, 14, 399-421 (1990)
[19] Figlewski, S., Forecasting volatility. Financial Markets, Institutions and Instruments, 6, 1-88 (1997)
[20] Fleming, J., The quality of market volatility forecasts implied by S&P 100 index option prices, Journal of Empirical Finance, 5, 317-345 (1998)
[21] Fleming, J.; Ostdiek, B.; Whaley, R. E., Predicting stock market volatility: a new measure, Journal of Futures Markets, 15, 265-302 (1995)
[22] Franses, P. H.; Van Dijk, D., Forecasting stock market volatility using (non-linear) GARCH models, Journal of Forecasting, 15, 229-235 (1995)
[23] Glosten, L. R.; Jagannathan, R.; Runkle, D. E., On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48, 1779-1801 (1993)
[24] Harvey, C. R.; Whaley, R. E., Dividends and S&P 100 index options, Journal of Futures Markets, 12, 123-137 (1992)
[25] Heynen, R. C.; Kat, H. M., Volatility prediction: a comparison of stochastic volatility, GARCH(1,1) and EGARCH(1,1) models, Journal of Derivatives, 2, 2, 50-65 (1994)
[26] Jorion, P., Predicting volatility in the foreign exchange market, Journal of Finance, 50, 507-528 (1995)
[27] Latane, H. A.; Rendleman, R. J., Standard deviations of stock price ratios implied in option prices, Journal of Finance, 31, 369-381 (1976)
[28] Nelson, D. B., Conditional heteroskedasticity in asset returns: a new approach, Econometrica, 59, 347-370 (1991) · Zbl 0722.62069
[29] Nelson, D. B., Filtering and forecasting with misspecified ARCH models I: getting the right variance with the wrong model, Journal of Econometrics, 52, 61-90 (1992) · Zbl 0761.62169
[30] Nelson, D. B.; Foster, D. P., Filtering and forecasting with misspecified ARCH models II: making the right forecast with the wrong model, Journal of Econometrics, 67, 303-335 (1995) · Zbl 0820.62098
[31] Taylor, S. J.; Xu, X., The incremental volatility information in one million foreign exchange quotations, Journal of Empirical Finance, 4, 317-340 (1997)
[33] Xu, X.; Taylor, S. J., Conditional volatility and the informational efficiency of the PHLX currency options markets, Journal of Banking and Finance, 19, 803-821 (1995)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.