A nonparametric approach to forecasting realized volatility
AbstractA 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.
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Bibliographic InfoPaper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 43.
Length: 16 pages
Date of creation: 12 May 2009
Date of revision:
Volatility; forecasts; forecast evaluation; model confidence set; nonparametric;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- G00 - Financial Economics - - General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-07-03 (All new papers)
- NEP-ECM-2009-07-03 (Econometrics)
- NEP-ETS-2009-07-03 (Econometric Time Series)
- NEP-FMK-2009-07-03 (Financial Markets)
- NEP-FOR-2009-07-03 (Forecasting)
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