Real‐Time Monitoring of Bubbles and Crashes
提出一种实时监测程序，在识别资产价格泡沫后，利用自回归模型和统计量快速检测崩盘，适用于美国房价等时间序列数据。
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 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.