股票市场危机时期的建模
Modeling of crisis periods in stock markets
摘要：
我们利用最近的计算框架来建模和检测股票市场的金融危机，以及加密货币市场的冲击事件，其特征是价格突然或严重下跌。我们的方法能够检测到法国工业股票市场从1929年崩盘开始的所有过去危机，包括1990年之后的金融危机（例如2000年的互联网泡沫破裂，2002年的股市低迷），以及加密货币市场的所有过去崩溃，即2018年，以及2020年由于COVID-19。我们利用copulae聚类，基于概率分布之间的距离，以验证框架的可靠性，我们表明，集群包含copulae从类似的市场状态，如正常状态，或危机。此外，我们提出了一种新的回归模型，可以成功地检测所有过去的事件，使用不到10%的信息，以前的框架需要。我们通过行业资产的历史数据来训练我们的模型，我们能够检测到加密货币市场中所有过去的冲击事件。我们的工具为我们的软件框架提供了必要的组件，可以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的冲击事件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
