Common Bubble Detection in Large Dimensional Financial Systems

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 ma...

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Main Authors: CHEN, Ye, PHILLIPS, Peter C. B., SHI, Shuping
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/soe_research/2696
https://ink.library.smu.edu.sg/context/soe_research/article/3695/viewcontent/SSRN_id3467372.pdf
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spelling sg-smu-ink.soe_research-36952023-11-10T02:35:46Z Common Bubble Detection in Large Dimensional Financial Systems CHEN, Ye PHILLIPS, Peter C. B. SHI, Shuping 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. 2023-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2696 info:doi/10.1093/jjfinec/nbab027 https://ink.library.smu.edu.sg/context/soe_research/article/3695/viewcontent/SSRN_id3467372.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Financial Econometrics Asian Studies Econometrics Macroeconomics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Financial Econometrics
Asian Studies
Econometrics
Macroeconomics
spellingShingle Financial Econometrics
Asian Studies
Econometrics
Macroeconomics
CHEN, Ye
PHILLIPS, Peter C. B.
SHI, Shuping
Common Bubble Detection in Large Dimensional Financial Systems
description 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.
format text
author CHEN, Ye
PHILLIPS, Peter C. B.
SHI, Shuping
author_facet CHEN, Ye
PHILLIPS, Peter C. B.
SHI, Shuping
author_sort CHEN, Ye
title Common Bubble Detection in Large Dimensional Financial Systems
title_short Common Bubble Detection in Large Dimensional Financial Systems
title_full Common Bubble Detection in Large Dimensional Financial Systems
title_fullStr Common Bubble Detection in Large Dimensional Financial Systems
title_full_unstemmed Common Bubble Detection in Large Dimensional Financial Systems
title_sort common bubble detection in large dimensional financial systems
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/soe_research/2696
https://ink.library.smu.edu.sg/context/soe_research/article/3695/viewcontent/SSRN_id3467372.pdf
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