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Asset Price Misalignments and the Role of Money and Credit

Authors


  • *We would like to thank Andrew Berg (IMF) for his help to conduct the pooled probit estimation and to calculate the heteroscedastic and autocorrelated corrected standard errors using EViews. Preliminary results were presented at a seminar workshop organized by the University of Tübingen on 29 January 2009 and at the ROME Workshop Spring 2009 held in Frankfurt on 5 June 2009. Very useful comments by Jürgen Stark, Philippe Moutot, Claudia Buch, Carsten Detken, Thomas Westermann and two anonymous referees are also gratefully acknowledged. The paper does not necessarily reflect the views of either the European Central Bank or the Frankfurt School of Finance and Management or the Hochschule Wismar.

Dieter Gerdesmeier
European Central Bank
Kaiserstrasse 29
60311 Frankfurt am Main
Germany
dieter.gerdesmeier@ecb.int Barbara Roffia
European Central Bank
Kaiserstrasse 29
60311 Frankfurt am Main
Germany
barbara.roffia@ecb.int Hans-Eggert Reimers
Fakultät fur Wiztschaft
Hochschule Wismar
Postfach 1210
23952 Wismar
hans-eggert.reimers@hs-wismar.de

Abstract

This paper contributes to the analysis on the properties of money and credit indicators for detecting asset price misalignments. After a literature review, the paper discusses several approaches useful for detecting asset price busts. Considering a sample of 17 Organization for Economic Cooperation and Development industrialized countries and the euro area over the period 1969 Q1–2008 Q3, an asset price composite indicator incorporating developments in both stock and house price markets is constructed and a criterion to identify the periods characterized by asset price busts is proposed. The empirical analysis is based on a pooled probit-type approach with several monetary, financial and real variables. According to statistical tests, credit aggregates (either in terms of annual changes or growth gap), changes in nominal long-term interest rates and investment-to-GDP ratios jointly with either house or stock price dynamics turn out to be the best indicators helping to forecast asset price busts up to eight quarters in advance. Some robustness checks indicate that both the method used to identify asset price busts and the choice of the binary variable are reliable.

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