Title: Real-time monitoring of bubbles and crashes
Authors: Emily Whitehouse - University of Sheffield (United Kingdom) [presenting]
Dave Harvey - University of Nottingham (United Kingdom)
Steve Leybourne - University of Nottingham (United Kingdom)
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 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. The 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 the 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.