Common Bubble Detection in Large Dimensional Financial Systems

44 Pages Posted: 10 Oct 2019

See all articles by Ye Zoe Chen

Ye Zoe Chen

Capital University of Economics and Business - International School of Economics and Management

Peter C. B. Phillips

Yale University - Cowles Foundation; University of Auckland; University of Southampton; Singapore Management University - School of Economics

Shuping Shi

Department of Economics, Macquarie University

Date Written: October 10, 2019

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 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 both I(1) and mildly explosive factors to capture normal and exuberant phases in such phenomena. The I(1) factor models the primary driving force of market fundamentals. The explosive factor models latent forces that underlie the formation of asset price bubbles, which typically exist only for subperiods of the sample. The paper provides an algorithm for testing the presence of and date-stamping the origination 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 consistency of a factor bubble detection process for the origination date 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 89 major cities in China over the period January 2003 to March 2013. Results suggest the presence of three common bubble episodes in what are known as China's Tier 1 and Tier 2 cities over the sample period. There appears to be little evidence of a common bubble in Tier 3 cities.

Keywords: Common Bubbles, Mildly Explosive Process, Factor Analysis, Date Stamping, Real Estate Markets

JEL Classification: C12, C13, C58

Suggested Citation

Chen, Ye Zoe and Phillips, Peter C. B. and Shi, Shuping, Common Bubble Detection in Large Dimensional Financial Systems (October 10, 2019). Macquarie Business School Research Paper. Available at SSRN: https://ssrn.com/abstract=3467372 or http://dx.doi.org/10.2139/ssrn.3467372

Ye Zoe Chen

Capital University of Economics and Business - International School of Economics and Management ( email )

Beijing
China

Peter C. B. Phillips

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States
203-432-3695 (Phone)
203-432-5429 (Fax)

University of Auckland ( email )

Private Bag 92019
Com. A room: 102
Auckland
New Zealand
+64 9 373 7599 x7596 (Phone)

University of Southampton

Southampton, SO17 1BJ
United Kingdom

Singapore Management University - School of Economics

90 Stamford Road
178903
Singapore

Shuping Shi (Contact Author)

Department of Economics, Macquarie University ( email )

New South Wales 2109
Australia

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