Modeling of crisis periods in stock markets

TL;DR

This paper presents a computational framework that effectively detects and models financial crises and shock events in stock and cryptocurrency markets, utilizing copulae clustering and a novel regression model for rapid identification.

Contribution

The paper introduces a new framework combining copulae clustering and a regression model that detects past crises with minimal data, improving speed and reliability over previous methods.

Findings

Successfully detected all historical crises in stock and cryptocurrency markets.

Validated the framework's reliability through copulae clustering.

Achieved rapid detection using less than 10% of the data required by prior methods.

Abstract

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.