Forecasting S&amp;P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns

Blair, Bevan; Poon, Ser‐Huang; Taylor, Stephen J.

doi:10.1016/s0304-4076(01)00068-9

Cited by 527 publications

(355 citation statements)

References 31 publications

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“…Using simultaneously information from stock returns and option prices by including the implied volatility in a GARCH-type model as in Blair et al (2001), or adding exogenous regressors that contain some incremental information about volatility such as trade volume (Brooks, 1998;Lamoureux and Lastrapes, 1990b), may lead to an increased forecast accuracy and deserves further attention. …”

Section: Resultsmentioning

confidence: 99%

Forecasting volatility

Gospodinov

and

Jiang

2006

Journal of Forecasting

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In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model-based volatility, and show that these volatility processes exhibit an extremely slow mean-reverting behavior and possible long memory. For this reason, we explicitly model the near-unit root behavior of volatility and construct median unbiased forecasts by approximating the finite-sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next-period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts.

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Section: Resultsmentioning

confidence: 99%

Forecasting volatility

Gospodinov

and

Jiang

2006

Journal of Forecasting

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“…Variables that have been shown to help predict volatility are trading volume, macroeconomic news announcements ( [58], [43], [17]), implied volatility from option prices and realized volatility ( [82], [11]), overnight returns ( [46], [68]), and after hours realized volatility ([21])…”

Section: Explanatory Variables In the Conditional Variance Equationmentioning

confidence: 99%

“…Recall the mean reverting form of the basic GARCH(1, 1) model in (11). In many empirical applications, the estimated mean reverting rateâ 1 +b 1 is often very close to 1.…”

Section: Two Component Garch Modelmentioning

confidence: 99%

Practical Issues in the Analysis of Univariate GARCH Models

Zivot

2009

Handbook of Financial Time Series

128

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“…Classic contributions include Day and Lewis (1992), Canina and Figlewski (1993), Lamoureux and Lastrapes (1993), Christensen and Prabhala (1998), Fleming (1998), and Blair, Poon, and Taylor (2001). The predictive power of option-implied equity volatility has been con…rmed recently by Busch, Christensen and Nielsen (2008), who compare option-implied forecasts with state-of-the-art realized volatility forecasts.…”

Section: Introductionmentioning

confidence: 99%