A Kernel Technique for Forecasting the Variance-Covariance Matrix

Ralf Becker (), Adam Clements and Robert O'Neill
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Ralf Becker: University of Manchester
Robert O'Neill: University of Manchester

No 66, NCER Working Paper Series from National Centre for Econometric Research

Abstract: The forecasting of variance-covariance matrices is an important issue. In recent years an increasing body of literature has focused on multivariate models to forecast this quantity. This paper develops a nonparametric technique for generating multivariate volatility forecasts from a weighted average of historical volatility and a broader set of macroeconomic variables. As opposed to traditional techniques where the weights solely decay as a function of time, this approach employs a kernel weighting scheme where historical periods exhibiting the most similar conditions to the time at which the forecast if formed attract the greatest weight. It is found that the proposed method leads to superior forecasts, with macroeconomic information playing an important role.

Keywords: Nonparametric; variance-covariance matrix; volatility forecasting; multivariate (search for similar items in EconPapers)
JEL-codes: C14 C32 C53 C58 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2010-10-28
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Working Paper: A Kernel Technique for Forecasting the Variance-Covariance Matrix (2010)
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