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Title: A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction Authors:  Verena Monschang - University of Muenster (Germany) [presenting]
Wilfling Bernd - University of Muenster (Germany)
Abstract: Mean-squared-forecast-error (MSE) accuracy improvements are investigated for linear-convex combination forecasts, whose components are pretreated by a procedure called Vector Autoregressive Forecast Error Modeling (VAFEM). Assuming that the forecast-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 a 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, and (3) the biasedness of the untreated forecast combination. We demonstrate the VAFEM capacity for realized-volatility forecasting, using S\&P 500 data.