Forecasting Financial Crashes: A Dynamic Risk Management Approach

Abstract

Since 2009, stock markets have resided in a long bull market regime. Passive investment strategies have succeeded during this low-volatility growth period. From 2018 on, however, there was a transition into a more volatile market environment interspersed by corrections increasing in amplitude and frequency. This calls for more adaptive dynamic risk management strategies, as opposed to static buy-and-hold strategies. To hedge against market drawdowns, the greatest source of risk that should accurately be estimated is crash risk. This article applies the Log-Periodic Power Law Singularity (LPPLS) model of endogenous asset price bubbles to monitor crash risk. The model is calibrated to 15 years market history for five relevant equity country indices. Particular emphasis is put on the US S&P 500 Composite Index and the recent market history of the "Corona" year 2020. The results show that relevant historical bubble events, including the Corona crash, could be detected with the model and derived indicators. Many of these events were predicted in advance in monthly reports by the Financial Crisis Observatory (FCO) at ETH Zurich. The Corona crash, as the most recent event of interest, is discussed in further detail. Our conclusion is that unsustainable price dynamics leading to an unstable bubble, fuelled by quantitative easing and other policies, already existed well before the pandemic started. Thus, the bubble bursting in February 2020 as a reaction to the Corona pandemic was of endogenous nature and burst in response to the exogenous Corona crisis, which was predictable to some degree based on the endogenous price dynamics. Following the crash, a fast recovery of the price to pre-crisis levels ensued in the following months. This lets us conclude that, as long as the underlying origins and the macroeconomic environment that created this bubble do not change, the bubble will continue to grow and potentially spread to other sectors. This may cause even more hectic market behaviour, overreaction and volatile corrections in the future.