A nonparametric approach to forecasting realized volatility

Adam Clements and Ralf Becker (ralf.becker@manchester.ac.uk)
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Ralf Becker: Manchester

No 43, NCER Working Paper Series from National Centre for Econometric Research

Abstract: A well developed literature exists in relation to modeling and forecasting asset return volatility. Much of this relate to the development of time series models of volatility. This paper proposes an alternative method for forecasting volatility that does not involve such a model. Under this approach a forecast is a weighted average of historical volatility. The greatest weight is given to periods that exhibit the most similar market conditions to the time at which the forecast is being formed. Weighting occurs by comparing short-term trends in volatility across time (as a measure of market conditions) by the application of a multivariate kernel scheme. It is found that at a 1 day forecast horizon, the proposed method produces forecasts that are significantly more accurate than competing approaches.

Keywords: Volatility; forecasts; forecast evaluation; model confidence set; nonparametric (search for similar items in EconPapers)
JEL-codes: C22 G00 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2009-05-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:qut:auncer:2009_56

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