Selecting forecasting models for portfolio allocation
AbstractTechniques for evaluating and selecting multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a competing set of forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood based loss function outperforms it competitors including those based on the given portfolio application. This result indicates that the particular application of forecasts is not necessarily the most effective approach under which to select models.
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Bibliographic InfoPaper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 85.
Length: 26 pages
Date of creation: 09 Aug 2012
Date of revision:
Multivariate volatility; portfolio allocation; forecast evaluation; model selection; model confidence set;
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-2012-08-23 (All new papers)
- NEP-ECM-2012-08-23 (Econometrics)
- NEP-FOR-2012-08-23 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ralf Becker & Adam Clements, 2007.
"Are combination forecasts of S&P 500 volatility statistically superior?,"
NCER Working Paper Series
17, National Centre for Econometric Research.
- Becker, Ralf & Clements, Adam E., 2008. "Are combination forecasts of S&P 500 volatility statistically superior?," International Journal of Forecasting, Elsevier, vol. 24(1), pages 122-133.
- Sébastien Laurent & Jeroen Rombouts & Francesco Violente, 2009.
"On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models,"
CIRANO Working Papers
- Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
- Sébastien Laurent & Jeroen V.K. Rombouts & Francesco Violante, 2009. "On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models," Cahiers de recherche 0948, CIRPEE.
- Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012.
"On the forecasting accuracy of multivariate GARCH models,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, 09.
- Sébastien Laurent & Jeroen V.K. Rombouts & Francesco Violante, 2010. "On the Forecasting Accuracy of Multivariate GARCH Models," Cahiers de recherche 1021, CIRPEE.
- LAURENT, Sébastien & ROMBOUTS, Jeroen V. K. & VIOLANTE, Francesco, 2010. "On the forecasting accuracy of multivariate GARCH models," CORE Discussion Papers 2010025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Massimiliano Caporin & Michael McAleer, 2010.
"Ranking Multivariate GARCH Models by Problem Dimension,"
Working Papers in Economics
10/34, University of Canterbury, Department of Economics and Finance.
- Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CARF F-Series CARF-F-219, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," "Marco Fanno" Working Papers 0124, Dipartimento di Scienze Economiche "Marco Fanno".
- Caporin, M. & McAleer, M.J., 2010. "Ranking multivariate GARCH models by problem dimension," Econometric Institute Report EI 2010-34, Erasmus University Rotterdam, Econometric Institute.
- Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CIRJE F-Series CIRJE-F-742, CIRJE, Faculty of Economics, University of Tokyo.
- Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
- Andrew Patton, 2006.
"Volatility Forecast Comparison using Imperfect Volatility Proxies,"
Research Paper Series
175, Quantitative Finance Research Centre, University of Technology, Sydney.
- Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
- Annastiina Silvennoinen & Timo Teräsvirta, 2008.
"Multivariate GARCH models,"
CREATES Research Papers
2008-06, School of Economics and Management, University of Aarhus.
- Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
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