Real-time monitoring procedures for early detection of bubbles

Asset price bubbles and crashes can have severe consequences for the stability of f inancial and economic systems. Policy-makers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible by our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and Bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States’ housing bubble that preceded the Global Financial Crisis could have been detected as early as 1999:Q1 by our new procedures.