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

Marcel Visser

MPRA Paper from University Library of Munich, Germany

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.

Keywords: volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 (search for similar items in EconPapers)
Date: 2008-10-14
New Economics Papers: this item is included in nep-ets, nep-for, nep-mst, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:11100

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