Testing stationarity of the detrended price return in stock markets

Arias-Calluari, Karina; Najafi, M. N.; Harré, Michael; Tang, Yaoyue; Alonso‐Marroquín, Fernando

doi:10.1016/j.physa.2021.126487

Cited by 6 publications

(3 citation statements)

References 60 publications

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“…of the autocorrelation function is 2H − 2, and the exponent for the power spectrum is α PS = 2H − 1, where H is the Hurst exponent [67,68], one finds that 0.65  H  0.8. This means that the time series is positively correlated.…”

Section: Resultsmentioning

confidence: 99%

Flicker noise in two-dimensional electron gas

Najafi¹,

Tizdast²,

Moghaddam³

et al. 2021

Phys. Scr.

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Using the method developed in a recent paper (2019 Euro. Phys. J. B 92 1–28) we consider 1/f noise in two-dimensional electron gas (2DEG). The electron coherence length of the system is considered as a basic parameter for discretizing the space, inside which the dynamics of electrons is described by quantum mechanics, while for length scales much larger than it the dynamics is semi-classical. For our model, which is based on the Thomas-Fermi–Dirac approximation, there are two control parameters: temperature T and the disorder strength (Δ). Our Monte Carlo studies show that the system exhibits 1/f noise related to the electronic avalanche size, which can serve as a model for describing the experimentally observed flicker noise in 2DEG. The power spectrum of our model scales with the frequency with an exponent in the interval 0.3 < α PS < 0.6. We numerically show that the electronic avalanches are scale-invariant with power-law behaviors in and out of the metal-insulator transition line.

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Section: Resultsmentioning

confidence: 99%

Flicker noise in two-dimensional electron gas

Najafi¹,

Tizdast²,

Moghaddam³

et al. 2021

Phys. Scr.

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show abstract

“…One of the earliest debates was over the best model to use for market fluctuations: cascading turbulence or a truncated Lévy flight [14,15,96,97]. This led to a long and fruitful series of investigations into the dynamics of the univariate time series of financial market indices [98][99][100][101], with recent contributions from our group in this area as well [102][103][104][105].…”

Section: Financial Markets and Systemic Risksmentioning

confidence: 99%

Complexity Economics in a Time of Crisis: Heterogeneous Agents, Interconnections, and Contagion

Harré

Eremenko

Glavatskiy

et al. 2021

Systems

Self Cite

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In this article, we consider a variety of different mechanisms through which crises such as COVID-19 can propagate from the micro-economic behaviour of individual agents through to an economy’s aggregate dynamics and subsequently spill over into the global economy. Our central theme is one of changes in the behaviour of heterogeneous agents, agents who differ in terms of some measure of size, wealth, connectivity, or behaviour, in different parts of an economy. These are illustrated through a variety of case studies, from individuals and households with budgetary constraints, to financial markets, to companies composed of thousands of small projects, to companies that implement single multi-billion dollar projects. In each case, we emphasise the role of data or theoretical models and place them in the context of measuring their inter-connectivity and emergent dynamics. Some of these are simple models that need to be `dressed’ in socio-economic data to be used for policy-making, and we give an example of how to do this with housing markets, while others are more similar to archaeological evidence; they provide hints about the bigger picture but have yet to be unified with other results. The result is only an outline of what is possible but it shows that we are drawing closer to an integrated set of concepts, principles, and models. In the final section, we emphasise the potential as well as the limitations and what the future of these methods hold for economics.

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“…Since the price series is non-stationary time-series data, the multifractal concept cannot be applied immediately [37]. This research, therefore, focuses on the prediction of the return series, which is stationary data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Volatility of the Stock Index with Deep Learning Using Asymmetric Hurst Exponents

Cho

Lee

2022

Fractal Fract

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The prediction of the stock price index is a challenge even with advanced deep-learning technology. As a result, the analysis of volatility, which has been widely studied in traditional finance, has attracted attention among researchers. This paper presents a new forecasting model that combines asymmetric fractality and deep-learning algorithms to predict a one-day-ahead absolute return series, the proxy index of stock price volatility. Asymmetric Hurst exponents are measured to capture the asymmetric long-range dependence behavior of the S&P500 index, and recurrent neural network groups are applied. The results show that the asymmetric Hurst exponents have predictive power for one-day-ahead absolute return and are more effective in volatile market conditions. In addition, we propose a new two-stage forecasting model that predicts volatility according to the magnitude of volatility. This new model shows the best forecasting performance regardless of volatility.

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Testing stationarity of the detrended price return in stock markets

Cited by 6 publications

References 60 publications

Flicker noise in two-dimensional electron gas

Flicker noise in two-dimensional electron gas

Complexity Economics in a Time of Crisis: Heterogeneous Agents, Interconnections, and Contagion

Forecasting the Volatility of the Stock Index with Deep Learning Using Asymmetric Hurst Exponents

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