Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns

Lancaster University Management School, Accounting and Finance Working Paper No. 99/014

35 Pages Posted: 29 Oct 1999

See all articles by Bevan Blair

Bevan Blair

Ingenious

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester; Alan Turing Institute

Stephen J. Taylor

Lancaster University - Department of Accounting and Finance

Multiple version iconThere are 2 versions of this paper

Date Written: November 2000

Abstract

The information content of implied volatilities and intra-day returns is compared, in the context of forecasting index volatility over horizons from one to twenty 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 five minute index returns. The in-sample estimates show that all relevant information is provided by the VIX index and hence there is no incremental information in high-frequency index returns. For out-of-sample forecasting, the VIX index and information from five minute returns provide forecasts that have similar accuracy.

JEL Classification: C22, C53, G13, G14

Suggested Citation

Blair, Bevan and Poon, Ser-Huang and Taylor, Stephen J., Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns (November 2000). Lancaster University Management School, Accounting and Finance Working Paper No. 99/014, Available at SSRN: https://ssrn.com/abstract=182128 or http://dx.doi.org/10.2139/ssrn.182128

Bevan Blair

Ingenious ( email )

London, W1F 9JG
United Kingdom

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester ( email )

Alliance Manchester Business School
Booth Street West
Manchester, Manchester M15 6PB
United Kingdom
+44 161 275 4031 (Phone)
+44 161 275 4023 (Fax)

HOME PAGE: http://www.manchester.ac.uk/research/Ser-huang.poon/

Alan Turing Institute ( email )

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

Stephen J. Taylor (Contact Author)

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom
+ 44 15 24 59 36 24 (Phone)
+ 44 15 24 84 73 21 (Fax)

HOME PAGE: http://www.lancs.ac.uk/staff/afasjt

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