Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&amp;P500 Market Correlation

Wand, Tobias; Heßler, Martin; Kamps, Oliver

doi:10.3390/e25091257

Cited by 3 publications

(2 citation statements)

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“…Our independent methodological CP approach adds new evidence to the general reasoning of locally (meta)stable market states. The existence of quasi-stationary states is further supported by an independent resilience analysis on the S&P500 mean market correlation time series which suggests locally stable economic states eventually operating on multiple time scales [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…The procedure chosen to calculate the mean market correlation of the S&P500 stock index is chosen analogously to the articles [ 1 , 12 ]. Confined to the considered time period between 1 January 1992 and 28 December 2012, we filter for companies that are included in the S&P500 index for at least 99.5% of the time and interpolate occasionally missing data.…”

Section: Methodsmentioning

confidence: 99%

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Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

Heßler,

Wand,

Kamps

2023

Entropy

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Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

Heßler,

Wand,

Kamps

2023

Entropy

Self Cite

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Anticipating critical transitions in multidimensional systems driven by time- and state-dependent noise

Morr,

Riechers,

Gorjão

et al. 2024

Phys. Rev. Research

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Anticipating bifurcation-induced transitions in dynamical systems has gained relevance in various fields of the natural, social, and economic sciences. Before the annihilation of a system's equilibrium point by means of a bifurcation, the system's internal feedbacks that stabilize the initial state weaken and eventually vanish, a process referred to as critical slowing down (CSD). In one-dimensional systems, this motivates the use of variance and lag-1 autocorrelation as indicators of CSD. However, the applicability of variance is limited to time- and state-independent driving noise, strongly constraining the generality of this CSD indicator. In multidimensional systems, the use of these indicators is often preceded by a dimension reduction in order to obtain a one-dimensional time series. Many common techniques for such an extraction of a one-dimensional time series generally incur the risk of missing CSD in practice. Here, we propose a data-driven approach based on estimating a multidimensional Langevin equation to detect local stability changes and anticipate bifurcation-induced transitions in systems with generally time- and state-dependent noise. Our approach substantially generalizes the conditions under which CSD can reliably be detected, as demonstrated in a suite of examples. In contrast to existing approaches, changes in deterministic dynamics can be clearly discriminated from changes in the driving noise using our method. This substantially reduces the risk of false or missed alarms of conventional CSD indicators in settings with time-dependent or multiplicative noise. In multidimensional systems, our method can greatly advance the understanding of the coupling between system components and can avoid risks of missing CSD due to dimension reduction, which existing approaches suffer from. Published by the American Physical Society 2024

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Comparative Analysis of Data Dynamics Based on Wavelet Coherence Using Higher-Order Moments

Al-Sharo,

Abu-Jassar,

Vasiurenko

et al. 2023

2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)

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Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation

Cited by 3 publications

References 65 publications

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation

Anticipating critical transitions in multidimensional systems driven by time- and state-dependent noise

Comparative Analysis of Data Dynamics Based on Wavelet Coherence Using Higher-Order Moments

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