Nonlinear dynamics and recurrence plots for detecting financial crisis

Abstract

Identification of financial bubbles and crisis is a topic of major concern since it is important to prevent collapses that can severely impact nations and economies. Our analysis deals with the use of the recently proposed 'delay vector variance' (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using wavelet-based surrogates. We exploit the concept of recurrence plots to study the stock market to locate hidden patterns, non-stationarity, and to examine the nature of these plots in events of financial crisis. In particular, the recurrence plots are employed to detect and characterize financial cycles. A comprehensive analysis of the feasibility of this approach is provided. We show that our methodology is useful in the diagnosis and detection of financial bubbles, which have significantly impacted economic upheavals in the past few decades.

An Empirical Test on Harrod’s Open Economy Dynamics

“…For example Fabretti and Ausloos (2005) [27] found cases on financial markets where RQA could detect a difference in state and recognize the critical regime such as a warning before a crash. Along this line Addo et al (2013) [28] who claims "the usefulness of recurrence plots in identifying, dating and explaining financial bubbles and crisis" and that recurrence plots show "that these plots are robust to extreme values, non stationarity and to the sample; are replicable and transparent; are adaptive to different time series and finally, can provide better chronology of financial cycles since it avoids revision of crisis dates through time". Orlando and Zimatore (2017-2019) [29][30][31] investigated the potential explanatory capability of RQA to show the structural characteristics of economic time series and on RQA anticipating signals.…”

Financial Stress Through Complexity Science

“…Also, prior to modelling financial indices, it is a prerequisite to establish the ground truth for the linear versus nonlinear and deterministic versus stochastic nature of the data, referred to as signal modality analysis. To this end, we employ nonparametric analyses using the methods of recurrence plots [25] and DVV [26], which examine the nature of the underlying generating mechanisms [27], a subject of Section III-C and Section III-D.…”

Forecasting abrupt changes in foreign exchange markets: method using dynamical network marker

“…We also recently learned that a recurrence plot contains almost all the information for the underlying dynamics because we can reproduce the rough shape of the original time series if the original time series is given as a time series with a fixed sampling frequency [15,16]. Thus, recurrence plots have been used in a variety of contexts in science and technology including climate [17,18], medicine [19], and economics [20,21], to name a few. An important quantity for a recurrence plot is the recurrence rate [5], which is the proportion of plotted places.…”