Advanced Search
MyIDEAS: Login to save this paper or follow this series

Can Emerging Asset Price Bubbles be Detected?

Contents:

Author Info

  • Jesús Crespo Crespo Cuaresma

Abstract

Bayesian Model Averaging techniques are used to analyse how robustly it is possible to identify factors that may lead to the bursting of asset price bubbles in OECD economies. A large set of variables put forward in the literature is assessed, as well as interactions of these variables with estimates of asset price misalignments to evaluate the importance of the different channels postulated by theory. The results indicate that asset price misalignments are not robust determinants of house price reversals unless their interaction with other characteristics of the economy (credit growth, population growth and interest rate developments) is taken into account. On the other hand, stock price reversals are affected by misalignments, as well as other real and monetary variables. Out-of-sample prediction exercises provide evidence that dealing explicitly with model uncertainty using Bayesian model averaging techniques leads to better forecasts of reversals in asset prices than relying on model selection. Conclusions regarding the importance of dealing quantitatively with model uncertainty are drawn to improve the anticipation of asset price reversals. Peut-on détecter les bulles naissantes des prix des actifs ? Des techniques de modèle bayésien en moyenne ont été utilisées pour analyser dans quelle mesure il est possible d’identifier de façon robuste les facteurs qui peuvent provoquer l’éclatement de bulles des prix des actifs dans les économies de l’OCDE. Un large ensemble de variables mises en avant par les spécialistes a été évalué, de même que les interactions de ces variables avec les estimations des désalignements des prix des actifs, le but étant de déterminer l’importance des différents canaux retenus sur le plan théorique. Les résultats montrent que les désalignements des prix des actifs ne constituent pas un déterminant fiable des retournements des prix immobiliers, sauf si l’on prend en compte leur interaction avec d’autres caractéristiques de l’économie (croissance du crédit, croissance démographique et évolution des taux d’intérêt). En revanche, les retournements des cours des actions subissent les effets des désalignements ainsi que ceux d’autres variables réelles et monétaires. Des exercices de prévision hors échantillon montrent qu’en traitant expressément l’incertitude du modèle par des techniques bayésiennes en moyenne, on obtient des prévisions des retournements des prix des actifs qui sont meilleures qu’en sélectionnant un modèle. Ce document tire une série de conclusions quant à l’importance d’un traitement quantitatif de l’incertitude liée à la modélisation, afin de pouvoir mieux anticiper les retournements des prix des actifs.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://dx.doi.org/10.1787/5kmdfmztmqtj-en
Download Restriction: no

Bibliographic Info

Paper provided by OECD Publishing in its series OECD Economics Department Working Papers with number 772.

as in new window
Length:
Date of creation: 01 Jun 2010
Date of revision:
Handle: RePEc:oec:ecoaaa:772-en

Contact details of provider:
Postal: 2 rue Andre Pascal, 75775 Paris Cedex 16
Phone: 33-(0)-1-45 24 82 00
Fax: 33-(0)-1-45 24 85 00
Email:
Web page: http://www.oecd.org
More information through EDIRC

Related research

Keywords: house prices; asset prices; stock prices; model uncertainty; model averaging; moyennes de modèles; cours des actions; incertitude des modèles; prix des actifs; prix immobiliers;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Helmut Herwartz & Konstantin A. Kholodilin, 2014. "In‐Sample and Out‐of‐Sample Prediction of stock Market Bubbles: Cross‐Sectional Evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 15-31, 01.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:oec:ecoaaa:772-en. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.