Whitehouse, E.J., Harvey, D.I. and Leybourne, S.J. (2022) Real-time monitoring of bubbles and crashes. Working Paper. Sheffield Economic Research Paper Series, 2022007 (2022007). Department of Economics, University of Sheffield ISSN 1749-8368
Abstract
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 the bubble and crash regimes modelled by explosive and stationary dynamics respectively. The first stage of our approach is to monitor for the presence of a bubble; conditional on having detected a bubble, 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, over which no bubble or crash is assumed to occur. Monte Carlo simulations suggest that our recommended procedure has a well-controlled false positive rate during a bubble regime, while also allowing very rapid detection of a crash when one occurs. Application to the US housing market demonstrates the efficacy of our procedure in rapidly detecting the house price crash of 2006.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). For reuse permissions, please contact the Author(s). |
Keywords: | Real-time monitoring; Bubble; Crash; Explosive autoregression; Stationary autoregression |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Apr 2022 13:00 |
Last Modified: | 21 Nov 2022 13:22 |
Published Version: | https://www.sheffield.ac.uk/economics/research/ser... |
Status: | Published |
Publisher: | Department of Economics, University of Sheffield |
Series Name: | Sheffield Economic Research Paper Series |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185770 |