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Real-Time Monitoring of Bubbles and Crashes

Whitehouse, Emily J.; Harvey, David I.; Leybourne, Stephen J.

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Emily J. Whitehouse


Given the financial and economic damage that can be caused by the collapse of an asset price bubble, it is of critical importance to rapidly detect the onset of a crash once a bubble has been identified. We develop a real-time monitoring procedure for detecting a crash episode in a time series. We adopt an autoregressive framework, with bubble and crash regimes modelled by explosive and stationary dynamics, respectively. The first stage of our approach is to monitor for a bubble; conditional on which, we monitor for a crash in real time as new data emerges. Our crash detection procedure employs a statistic based on the different signs of the means of the first differences associated with explosive and stationary regimes, and critical values are obtained using a training period of data. We show that the procedure has desirable asymptotic properties in terms of its ability to rapidly detect a crash while never indicating a crash earlier than one occurs. Monte Carlo simulations further demonstrate that our procedure can offer a well-controlled false positive rate during a bubble regime. Application to the US housing market demonstrates the efficacy of our procedure in rapidly detecting the house price crash of 2006.


Whitehouse, E. J., Harvey, D. I., & Leybourne, S. J. (2023). Real-Time Monitoring of Bubbles and Crashes. Oxford Bulletin of Economics and Statistics, 85(3), 482-513.

Journal Article Type Article
Acceptance Date Nov 16, 2022
Online Publication Date Jan 27, 2023
Publication Date 2023-06
Deposit Date Nov 23, 2022
Publicly Available Date Jan 28, 2025
Journal Oxford Bulletin of Economics and Statistics
Print ISSN 0305-9049
Electronic ISSN 1468-0084
Peer Reviewed Peer Reviewed
Volume 85
Issue 3
Pages 482-513
Keywords Real-time monitoring; Bubble; Crash; Explosive autoregression; Stationary autoregression
Public URL
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