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Abstract

This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics into a forecast equation for daily volatility. Analysis of S&P 500 index tick data over the years 1988-2006 shows that taking into account the downward movements improves forecast accuracy significantly. The R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for in-sample and out-of-sample prediction.

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File URL: http://mpra.ub.uni-muenchen.de/11100/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11100.

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Date of creation: 14 Oct 2008
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Handle: RePEc:pra:mprapa:11100

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Related research

Keywords: volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data;

Find related papers by JEL classification:

• C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting Models; Simulation Methods
• C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
• G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

This paper has been announced in the following NEP Reports:

• NEP-ALL-2008-10-21 (All new papers)
• NEP-ETS-2008-10-21 (Econometric Time Series)
• NEP-FOR-2008-10-21 (Forecasting)
• NEP-MST-2008-10-21 (Market Microstructure)
• NEP-ORE-2008-10-21 (Operations Research)
• NEP-RMG-2008-10-21 (Risk Management)

References

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