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

Visser, Marcel P. (2008): Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure.

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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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