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.

中文翻译：

---

股票市场危机时期的建模

我们利用最新的计算框架来建模和检测股票市场中的金融危机以及加密货币市场中的震荡事件，这些事件的特征是价格突然或严重下跌。从1929年崩溃开始，我们的方法设法检测出法国工业股票市场过去的所有危机，包括1990年以后的金融危机（例如2000年的互联网泡沫破灭，2002年的股市低迷）以及加密货币中的所有过去崩溃市场，即2018年，以及由于covid-19而在2020年。我们基于概率分布之间的距离利用copulae聚类，以验证框架的可靠性。我们表明，集群包含来自相似市场状态（例如正常状态或危机）的系动词。而且，我们提出了一种新颖的回归模型，该模型可以使用不到先前框架所需信息的10％来成功检测所有过去的事件。我们通过有关行业资产的历史数据来训练我们的模型，并且我们能够检测到加密货币市场中所有过去的冲击事件。我们的工具提供了我们软件框架的基本组件，可快速可靠地检测甚至预测数百种资产的股票和加密货币市场中的冲击事件。