Forecasting financial crises is difficult. The data comprises a limited set of observed instances and signals a warning only when it is already too late to intervene. Moreover, the early warning models are complex and difficult to distil into simple, transparent indicators for macroprudential authorities. But can we use machine learning to model the crises? A new staff working paper from the Bank of England tries to find out.
The Set Up
They use a database that covers 2,499 observations across 17 countries between 1870 and 2016. For the purpose of testing, they exclude the crisis year itself and the following four years, as well 1914-1918 and 1930-1945 (the Great Depression and the two World Wars). This allows them to more cleanly use non-crisis period to predict the crisis. For their models to be successful, it has to predict the crisis one to two years before it erupts.
For the predictive indicators, they look at:
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