Recurrence Quantification Analysis of Financial Market Crashes and Crises

TL;DR

This paper applies Recurrence Quantification Analysis (RQA), a nonlinear method suitable for nonstationary data, to study various historical financial market crashes and crises, proposing it as a tool for early detection and analysis.

Contribution

The study demonstrates the effectiveness of RQA in analyzing financial crashes and crises, highlighting its potential as a tool for monitoring and predicting critical market events.

Findings

RQA successfully identified patterns associated with market crashes.

The LAM measure shows promise as an early warning indicator.

RQA provides insights into the complex dynamics of financial crises.

Abstract

Financial markets are systems with the complex behavior, that can be hardly analyzed by means of linear methods. Recurrence Quantification Analysis (RQA) is a nonlinear methodology, which is able to work with the nonstationary and short data series. Thus, we apply RQA for the studying of the critical events on financial markets. For the present research, stock crashes of DJI 1929; DJI, NYSE and S&P500 1987; NASDAQ 2000; HSI 1994, 1997 and Spanish 1992, Portuguese 1992, British 1992, German 1992, Italian 1992, Mexican 1994, Brazilian 1999, Indonesian 1997, Thai 1997, Malaysian 1997, Philippine 1997, Russian 1998, Turkish 2001, Argentine 2002 currency devaluations were taken. The recent world financial crisis of 2007-2010 was considered as well. The possibility of LAM measure to serve as a tool for the revealing, monitoring, analysing and precursoring of financial bubbles, crises and crashes was asserted.