A nonparametric approach to forecasting realized volatility
Adam Clements and
Ralf Becker ()
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Ralf Becker: Manchester
No 43, NCER Working Paper Series from National Centre for Econometric Research
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)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:qut:auncer:2009_56
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