Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements

Eugenie Hol, Siem Jan Koopman and Borus Jungbacker

No 342, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard \& Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. A stationary bootstrap procedure is required for computing the test statistic and its p-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts

Keywords: Generalised autoregressive conditional heteroskedasticity model; Long memory model; Realised volatility; Stochastic volatility model; Superior predictive ability; Unobserved components (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Date: 2004-08-11
References: Add references at CitEc
Citations: View citations in EconPapers (19)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:342

Access Statistics for this paper

More papers in Computing in Economics and Finance 2004 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().