Coupled Criticality Analysis of Inflation and Unemployment

Lai, Zahra Koohi; Namaki, Ali; Hosseiny, Ali; Jafari, G. R.; Ausloos, Marcel

doi:10.3390/e23010042

Cited by 5 publications

(3 citation statements)

References 87 publications

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“…For the non-Gaussian case, with the λ = 0 signal will represent multifractal behavior. In this section, using only a set of few variables, we apply multifractal statistics to model the increment in fluctuations of the data [44,45].…”

Section: Multifractal Formalismmentioning

confidence: 99%

Quantum Bohmian-Inspired Potential to Model Non–Gaussian Time Series and Its Application in Financial Markets

Hosseini

Tajik

Lai

et al. 2023

Entropy

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We have implemented quantum modeling mainly based on Bohmian mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study was to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying the quantum potential beyond the microscopic world, we compute quantum potentials for the S&P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

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Section: Multifractal Formalismmentioning

confidence: 99%

Quantum Bohmian-Inspired Potential to Model Non–Gaussian Time Series and Its Application in Financial Markets

Hosseini

Tajik

Lai

et al. 2023

Entropy

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“…For the non Gaussian case with the λ = 0 signal will represent multifractality behavior. In this section, using only a set of few variables, we apply multifractal statistics to model the increment of fluctuations of the data 31,32 .…”

Section: /7mentioning

confidence: 99%

Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets

Hosseini¹,

Tajik²,

Lai³

et al. 2022

Preprint

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We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. We firstly propose how compared to normal densities, our target time series seem to be associated with a higher number of rare events, and Gaussian statistics tend to underestimate these events' frequency drastically. To this end, we suggest that by imposing Gaussian densities to the natural processes, one will seriously neglect the existence of extreme events in many circumstances. The central question of our study concerns the consideration of the effects of these rare events in the corresponding probability densities and studying their role from the point of view of quantum measurements. To model the non-Gaussian behavior of these time-series, we utilize the multifractal random walk (MRW) approach and control the non-Gaussianity parameter λ accordingly. Using the framework of quantum mechanics, we then examine the role of λ in quantum potentials derived for these time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for the rare events. We thus conclude that these events could generate a potential barrier that the system, lingering in a non-Gaussian high-frequency region, encounters, and their role becomes more prominent when it comes to transversing this barrier. In this study, as an example of the application of quantum potential outside of the micro-world, we compute the quantum potentials for the S&P financial market time series to verify the presence of rare events in the non-Gaussian densities for this real data and remark the deviation from the Gaussian case.

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“…The capital market is one of the most essential parts of the economy (Namaki, et al, 2021). In this market, the formation of price bubbles and the subsequent fall of prices is considered as one of the most critical disorders (Koohi, et al, 2020). This phenomenon has always been regarded as an important topic for scientific centers such as universities and activists of these markets as well as policymakers (Namaki, Nazari, & Gaeeini, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detection of Bubbles in Tehran Stock Exchange Using Log-Periodic Power-Low Singularity Model

Namaki

Haghgoo

2021

IJFIFSA

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One of the essential factors that lead to severe disruptions in financial markets is price bubbles and subsequent crashes. Numerous models for detecting bubbles have been developed, one of which (LPPLS) has lately attracted considerable interest. This study aims to utilize this model to detect price bubbles in Tehran Stock Exchange's index (TEDPIX). Confidence multi-scale indicators for this model are presented by fitting the LPPLS model to the data of the TSE index from 2009 through 2020. The bubble is detected when the number of fits that are in our filter conditions increases which means the growth of the indicator's value. By applying this method on TSE data two significant crashes in 2013 and 2020 are detected. The proposed technique can be useful for market participants to detect financial crashes and bubbles.

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Coupled Criticality Analysis of Inflation and Unemployment

Cited by 5 publications

References 87 publications

Quantum Bohmian-Inspired Potential to Model Non–Gaussian Time Series and Its Application in Financial Markets

Quantum Bohmian-Inspired Potential to Model Non–Gaussian Time Series and Its Application in Financial Markets

Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets

Detection of Bubbles in Tehran Stock Exchange Using Log-Periodic Power-Low Singularity Model

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