Journal of Time Series Analysis has just published a special issue on Recent Developments in Time-Series Methods for Detecting Bubbles and Crashes, guest edited by David I. Harvey and Stephen J. Leybourne.

Financial and economic bubbles, along with their inevitable crashes, can have a significant impact on investment outcomes and a country’s macroeconomic performance. From historical speculative behavior to modern asset price surges, identifying and measuring these phenomena has posed a crucial challenge for investors and policymakers alike. Examples such as the Dot-Com bubble that originated in the mid-1990s, the US housing market bubble of the late 1990s and early 2000s, and the Bitcoin price bubbles since the mid-2010s, underscore the need for robust econometric methods to detect the presence and timing of bubble behavior, either historically or in real time.

Read the introduction for free here.