A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction
Monschang Verena, Wilfling Bernd
Working paperAbstract
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 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 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.
Details about the publication
Authors from the University of Münster
| Monschang, Verena | Chair of Empirical Economics |
| Wilfling, Bernd | Professur für Volkswirtschaftslehre, empirische Wirtschaftsforschung (Prof. Wilfling) |
Promotionen, aus denen die Publikation resultiert
| Contributions to Forecasting and Hypothesis Testing with Application to Financial-Market Data Candidate: Monschang, Verena | Supervisors: Wilfling, Bernd; Trede, Mark | Reviewers: Trede, Mark; Wilfling, Bernd Period of time: 01/04/2017 - 01/07/2022 Doctoral examination procedure finished at: Doctoral examination procedure at University of Münster |