ﻻ يوجد ملخص باللغة العربية
The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurcation transitions, in particular, the catastrophic slowing down (analogous to the critical slowing down). All these things were considered on a well-defined example of financial markets of small and middle to large capitalization. The catastrophic bifurcation transition seems to be connected with the question of whether the early-warning signals are present in financial markets. This question continues to fascinate both the research community and the general public. Interestingly, such early-warning signals have recently been identified and explained to be a consequence of a catastrophic bifurcation transition phenomenon observed in multiple physical systems, e.g. in ecosystems, climate dynamics and in medicine (epileptic seizure and asthma attack). In the present work we provide an analogical, positive identification of such phenomenon by examining its several different indicators in the context of a well-defined daily bubble; this bubble was induced by the recent worldwide financial crisis on typical financial markets of small and middle to large capitalization.
Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hy
A perspective is taken on the intangible complexity of economic and social systems by investigating the underlying dynamical processes that produce, store and transmit information in financial time series in terms of the textit{moving average cluster
We study the crash dynamics of the Warsaw Stock Exchange (WSE) by using the Minimal Spanning Tree (MST) networks. We find the transition of the complex network during its evolution from a (hierarchical) power law MST network, representing the stable
As more and more data being created every day, all of it can help take better decisions with data analysis. It is not different from data generated in financial markets. Here we examine the process of how the global economy is affected by the market
We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, an