A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction

Verena Monschang and Bernd Wilfling

No 9722, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

Abstract: We investigate mean-squared-forecast-error (MSE) accuracy improvements for linear-convex combination forecasts, whose components are pretreated by a procedure called 'Vector Autoregressive Forecast Error Modeling' (VAFEM). Assuming that the fore-cast-error series of the individual forecasts are governed by a stable VAR process under classic conditions, we obtain the following results: (i) VAFEM treatment bias-corrects all individual and linear-convex combination forecasts. (ii) Any VAFEM-treated combination has smaller theoretical MSE than its untreated analogue, if the VAR parameters are known. (iii) In empirical applications, VAFEM gains depend on (1) in-sample sizes, (2) out-of-sample forecast horizons, (3) the biasedness of the untreated forecast combination. We demonstrate the VAFEM capacity for realized-volatility forecasting, using S&P 500 data.

Keywords: Combination forecasts; mean-squared-error loss; VAR forecast-error molding; multivariate least squares estimation (search for similar items in EconPapers)
JEL-codes: C10 C32 C51 C53 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2022-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
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