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

25 Pages Posted: 1 Mar 2002

See all articles by Martin Martens

Martin Martens

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 P.E. 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 P.E. Martens

Robeco Asset Management ( email )

Weena 850
Rotterdam, 3014 DA
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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