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Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure

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  • Visser, Marcel P.

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

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