Time-varying Hurst exponent for US stock markets

Alvarez-Ramı́rez, José; Álvarez, Jesús; Rodríguez, Eduardo; Fernández‐Anaya, Guillermo

doi:10.1016/j.physa.2008.06.056

Cited by 187 publications

(89 citation statements)

References 16 publications

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“…However, as sample size was relatively small, the authors had some caution with their conclusions. Alvarez-Ramirez et al (2008) applied DFA to evaluate the possibility of forecasting ability in oil prices, concluding that, in short periods of time, there was some persistence in the correlations, but for longer horizons (time spans greater than 25 days), such relationship ceased to exist. Analysing 26 different stocks of the NYSE on a given day, Mariani et al (2009) concluded that 19 out of 26 titles presented evidence of long memory, of which 18 had persistent behaviour and only one was anti-persistent.…”

Section: Detrended Fluctuation Analysismentioning

confidence: 99%

Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece

Ferreira

Dionísio

2014

Applied Financial Economics

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This article uses several tests to analyse serial dependence in financial data, trying to confirm the existence of some kind of nonlinear dependence in stock markets. In an attempt to provide a better explanation of the behaviour of stock markets, we used tests based on mutual information and detrended fluctuation analysis (DFA). Applying these tests to the series of stock market indexes of 10 countries, we concluded for the absence of linear autocorrelation. However, with other tests, we found nonlinear serial dependence that affects the rates of return. With DFA, we found out that most return rate series have long-range dependence, which appears to be more pronounced for Spain, Greece and Portugal. To confirm the inefficiency of those markets, based on our results, we should prove the existence of abnormal profits.

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Section: Detrended Fluctuation Analysismentioning

confidence: 99%

Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece

Ferreira

Dionísio

2014

Applied Financial Economics

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“…Other finding was that only India had shown the upward Hurst trend in the time period. Furthermore, [10] implemented Hurst Exponent analysis for investigating efficiency of Brazilian foreign exchange market and [11] used the method to analyze dynamical behaviour of US stock markets Dow Jones.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Indices of BRIC economies: Explored for Non Linear Dynamics and Volatility

Karamchandani¹,

Mohadikar²,

Jain³

2014

IOSRJEF

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“…For example, H = 1/2 has been associated with the failure of the efficient market hypothesis (EMH) [14,15], namely that asset prices do not fully reflect all pertinent information on the market [16][17][18].…”

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confidence: 99%

Anomalous scaling of stochastic processes and the Moses effect

et al. 2017

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The state of a stochastic process evolving over a time t is typically assumed to lie on a normal distribution whose width scales like t 1 2 . However, processes where the probability distribution is not normal and the scaling exponent differs from 1 2 are known. The search for possible origins of such "anomalous" scaling and approaches to quantify them are the motivations for the work reported here. In processes with stationary increments, where the stochastic process is time-independent, auto-correlations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the Joseph effect the Noah effect, respectively. If the increments are non-stationary, then scaling of increments with t can also lead to anomalous scaling, a mechanism we refer to as the Moses effect. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes are used to illustrate each effect. Intraday financial time series data is analyzed, revealing that its anomalous scaling is due only to the Moses effect. In the context of financial market data, we reiterate that the Joseph exponent, not the Hurst exponent, is the appropriate measure to test the efficient market hypothesis.

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Time-varying Hurst exponent for US stock markets

Cited by 187 publications

References 16 publications

Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece

Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece

Stock Indices of BRIC economies: Explored for Non Linear Dynamics and Volatility

Anomalous scaling of stochastic processes and the Moses effect

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