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Can Emerging Asset Price Bubbles be Detected?


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  • Jesús Crespo Crespo Cuaresma


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.

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Bibliographic Info

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

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Date of creation: 01 Jun 2010
Date of revision:
Handle: RePEc:oec:ecoaaa:772-en

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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;

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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.
  2. Leroi Raputsoane, 2014. "Disaggregated Credit Extension and Financial Distress in South Africa," Working Papers 435, Economic Research Southern Africa.


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