en
Scientific article
English

Stock and bond return predictability: the discrimination power of model selection criteria

Published inComputational statistics & data analysis, vol. 50, no. 6, p. 1478-1495
Publication date2006
Abstract

The discrimination power of well-known model selection criteria is analyzed when the R-squared is low as in typical asset return predictability studies. It turns out that the discrimination power is low in this situation and this may explain, already in a simple i.i.d. setup, why often in-sample predictability, but no out-of-sample predictability is found. In particular it is possible to give another interpretation to the results of the well-cited Bossaerts and Hillion (Rev. Financial Stud. 12 (1999) 405–428) study. As a consequence, model selection criteria are put in a testing framework and a bootstrap-based procedure is proposed as a diagnostic tool to construct the class of models which are statistically indistinguishable from the best model chosen by a model selection criterion. In an empirical illustration the Pesaran and Timmerman (J. Finance 50 (1995) 1201–1228) results are reanalyzed and it turns out that in this case this class of models can be large. Finally it is shown that similar problems arise in a more hidden way in the context of recent model uncertainty studies using Bayesian model selection criteria.

Keywords
  • Model selection
  • Akaike
  • Schwarz
  • Stock return predictability
  • Forecasting
  • Asset pricing
  • Factor model
  • Risk model
  • Bootstrap
  • Bayesian model selection
Citation (ISO format)
DELL’AQUILA, Rosario, RONCHETTI, Elvezio. Stock and bond return predictability: the discrimination power of model selection criteria. In: Computational statistics & data analysis, 2006, vol. 50, n° 6, p. 1478–1495. doi: 10.1016/j.csda.2005.01.001
Main files (1)
Article (Published version)
accessLevelPrivate
Identifiers
ISSN of the journal0167-9473
632views
0downloads

Technical informations

Creation09/12/2012 7:50:00 PM
First validation09/12/2012 7:50:00 PM
Update time03/14/2023 5:41:01 PM
Status update03/14/2023 5:41:01 PM
Last indexation05/02/2024 12:39:30 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack