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Predicting Financial Volatility: High-Frequency Time-Series Forecasts Vis-a-Vis Implied Volatility

25 Pages Posted: 1 Mar 2002  

Martin Martens

Erasmus University Rotterdam (EUR); Robeco Asset Management

Jason Zein

UNSW Business School; Financial Research Network (FIRN)

Date Written: February 21, 2002

Abstract

Recent evidence suggests option implied volatility provides better forecasts of financial volatility than time-series models based on historical daily returns. In particular it is found that daily GARCH forecasts have no or little incremental information over that already contained in implied volatilities. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and the latest proposed model for volatility, a long memory model. The results indicate that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with implied volatility and sometimes even outperform implied volatility.

Keywords: Implied volatility, long memory, high-frequency data

JEL Classification: G14

Suggested Citation

Martens, Martin and Zein, Jason, Predicting Financial Volatility: High-Frequency Time-Series Forecasts Vis-a-Vis Implied Volatility (February 21, 2002). Available at SSRN: https://ssrn.com/abstract=301382 or http://dx.doi.org/10.2139/ssrn.301382

Martin Martens

Erasmus University Rotterdam (EUR) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands
+31 10 408 1253 (Phone)
+31 10 408 9162 (Fax)

Robeco Asset Management ( email )

Rotterdam, 3011 AG
Netherlands

Jason Zein (Contact Author)

UNSW Business School ( email )

Sydney, NSW 2052
Australia
+61 2 93855858 (Phone)
+61 2 93855858 (Fax)

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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