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Title: Common Bubble Detection in Large Dimensional Financial Systems
Abstract

Price bubbles in multiple assets are sometimes nearly coincident in occurrence. Such near-coincidence is strongly suggestive of co-movement in the associated asset prices and is likely driven by certain factors that are latent in the financial or economic system with common effects across several markets. Can we detect the presence of such common factors at the early stages of their emergence? To answer this question, we build a factor model that includes I(1), mildly explosive, and stationary factors to capture normal, exuberant, and collapsing phases in such phenomena. The I(1) factor models the primary driving force of market fundamentals. The explosive and stationary factors model latent forces that underlie the formation and destruction of asset price bubbles, which typically exist only for subperiods of the sample. The article provides an algorithm for testing the presence of and date-stamping the origination and termination of price bubbles determined by latent factors in a large-dimensional system embodying many markets. Asymptotics of the bubble test statistic are given under the null of no common bubbles and the alternative of a common bubble across these markets. We prove the consistency of a factor bubble detection process for the origination and termination dates of the common bubble. Simulations show good finite sample performance of the testing algorithm in terms of its successful detection rates. Our methods are applied to real estate markets covering eighty-nine major cities in China over the period January 2005 to December 2008. Results suggest the presence of a common bubble episode in what are known as China’s Tier 1 and Tier 2 cities from June 2007 to February 2008. There is also a common bubble episode in Tier 3 cities but of shorter duration.

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NSF-PAR ID:
10451606
Author(s) / Creator(s):
; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of Financial Econometrics
Volume:
21
Issue:
4
ISSN:
1479-8409
Page Range / eLocation ID:
p. 989-1063
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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