The scale-dependent market trend: Empirical evidences using the lagged DFA method

Li, Daye; Kou, Zhun; Sun, Qiankun

doi:10.1016/j.physa.2015.03.034

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…We apply the delampertized fBm to financial data. We indeed found one other paper proving empirically, with the Hurst approach, that some financial time series are persistent at low scales and anti-persistent at higher scales [23]. We confirm here this assertion for high-frequency foreign exchange rates and we provide a mathematical framework for this stylized fact.…”

Section: Introductionsupporting

confidence: 84%

Hurst Exponents and Delampertized Fractional Brownian Motions

Garcin

2019

Int. J. Theor. Appl. Finan.

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The inverse Lamperti transform of a fractional Brownian motion (fBm) is a stationary process. We determine the empirical Hurst exponent of such a composite process with the help of a regression of the log absolute moments of its increments, at various scales, on the corresponding log scales. This perceived Hurst exponent underestimates the Hurst exponent of the underlying fBm. We thus encounter some time series having a perceived Hurst exponent lower than [Formula: see text], but an underlying Hurst exponent higher than [Formula: see text]. This paves the way for short- and medium-term forecasting. Indeed, in such series, mean reversion predominates at high scales, whereas persistence is overriding at lower scales. We propose a way to characterize the Hurst horizon, namely a limit scale between these opposite behaviors. We show that the delampertized fBm, which mixes persistence and mean reversion, is relevant for financial time series, in particular for high-frequency foreign exchange rates. In our sample, the empirical Hurst horizon is always above 1[Formula: see text]h and 23[Formula: see text]min.

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Section: Introductionsupporting

confidence: 84%

Hurst Exponents and Delampertized Fractional Brownian Motions

Garcin

2019

Int. J. Theor. Appl. Finan.

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“…The results imply Q4 29 that for self-similar processes, the local properties can only partly be reflected in the global properties, and the uni-fractal 30 is only an approximation for the return series of the real financial markets. The local time-dependent Hurst exponent has 31 been developed and used to predict the rupture or crash point with the necessary conditions and reveal the local statistical 32 properties [41,45]. The value of the Hurst exponent varies with the width of the observation box, and the local statistical 33 properties change notably with the time and scales.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, we empirically investigate whether the width of the moving window affects the prediction based on 44 the local Hurst exponent and how precise the prediction is on different scales. 45 The empirical research above indicates the statistical characteristics of the real markets. To obtain a broader picture 46 of the relationship between the Hurst exponent and the transaction cost, we use the Monte Carlo simulation to display a finer-grained frontier of efficient markets with transaction costs, and various degrees of long memory from the simulating 48 markets are examined.…”

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confidence: 99%

The long memory and the transaction cost in financial markets

Nishimura

Men

2016

Physica A: Statistical Mechanics and its Applications

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“…It is in this context that Peters (1991Peters ( , 1994 proposes the Fractal Market Hypothesis (FMH). FMH is based on several of these features that are observed in financial markets (Li et al, 2015). It is in opposition to the more mainstream Efficient Market Hypothesis (EMH) of Fama (1965Fama ( , 1970.…”

Section: First Approaches To Analyse Stock Market Comovementsmentioning

confidence: 99%

Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients

Ferreira

Tilfani

Pereira

et al. 2021

Econometric Research in Finance

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This paper aims to analyse the connectivity of 13 stock markets, between 1998 and 2019, with a time-varying proposal, to evaluate evolution of the linkage between these markets over time. To do so, we propose to use a network built based on the correlation coefficients from the Detrended Cross-Correlation Analysis, using a sliding windows approach. Besides allowing for analysis over time, our approach also enables us to verify how the network behaves for different time scales, which enriches the analysis. We use two different properties of networks: global efficiency and average grade, to measure the network’s connectivity over time. We find that the markets under analysis became more connected before the subprime crisis, with this behavior extending even after the Eurozone crisis, showing that during extreme events there is an increase in financial risk, as found in the international literature.

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The scale-dependent market trend: Empirical evidences using the lagged DFA method

Cited by 13 publications

References 26 publications

Hurst Exponents and Delampertized Fractional Brownian Motions

Hurst Exponents and Delampertized Fractional Brownian Motions

The long memory and the transaction cost in financial markets

Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients

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