  We exploit a recent computational framework to model and detect financial
crises in stock markets, as well as shock events in cryptocurrency markets,
which are characterized by a sudden or severe drop in prices. Our method
manages to detect all past crises in the French industrial stock market
starting with the crash of 1929, including financial crises after 1990 (e.g.
dot-com bubble burst of 2000, stock market downturn of 2002), and all past
crashes in the cryptocurrency market, namely in 2018, and also in 2020 due to
covid-19. We leverage copulae clustering, based on the distance between
probability distributions, in order to validate the reliability of the
framework; we show that clusters contain copulae from similar market states
such as normal states, or crises. Moreover, we propose a novel regression model
that can detect successfully all past events using less than 10% of the
information that the previous framework requires. We train our model by
historical data on the industry assets, and we are able to detect all past
shock events in the cryptocurrency market. Our tools provide the essential
components of our software framework that offers fast and reliable detection,
or even prediction, of shock events in stock and cryptocurrency markets of
hundreds of assets.
