Long correlations and truncated Levy walks applied to the study Latin-American market indices

Jaroszewicz, S.; Mariani, Maria C.; Ferraro, Marta B.

doi:10.1016/j.physa.2005.04.003

Cited by 20 publications

(18 citation statements)

References 24 publications

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“…Ausloos (2000) also analysed foreign exchange markets, studying 13 different exchange rates, and found that, in most cases, there was evidence of long-term correlations. Jaroszewicz et al (2005) developed a pioneer work because they analysed Latin American indexes. In their study, the authors found evidence of correlations of long-term returns primarily in absolute returns and in relation to return rates.…”

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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“…Usually, the distribution is Gaussian or a truncated Levy flight (to express the leptokurtic nature of the empirical data) or other similar distributions. 2,[10][11][12][13][14][15][16][17][18] According to these models, the price is the result of a random walk, where the increments are randomly chosen.…”

Section: Introductionmentioning

confidence: 99%

Hidden temporal order unveiled in stock market volatility variance

et al. 2011

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When analyzed by standard statistical methods, the time series of the daily return of financial indices appear to behave as Markov random series with no apparent temporal order or memory. This empirical result seems to be counter intuitive since investor are influenced by both short and long term past market behaviors. Consequently much effort has been devoted to unveil hidden temporal order in the market dynamics. Here we show that temporal order is hidden in the series of the variance of the stocks volatility. First we show that the correlation between the variances of the daily returns and means of segments of these time series is very large and thus cannot be the output of random series, unless it has some temporal order in it. Next we show that while the temporal order does not show in the series of the daily return, rather in the variation of the corresponding volatility series. More specifically, we found that the behavior of the shuffled time series is equivalent to that of a random time series, while that of the original time series have large deviations from the expected random behavior, which is the result of temporal structure. We found the same generic behavior in 10 different stock markets from 7 different countries. We also present analysis of specially constructed sequences in order to better understand the origin of the observed temporal order in the market sequences. Each sequence was constructed from segments with equal number of elements taken from algebraic distributions of three different slopes.

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“…50 Abruptly truncated Lévy distribution (Jaroszewicz, Mariani, et al 2005); exponentially truncated Lévy distribution (Matsushita, Rathie, et al 2003); gradually truncated Lévy distribution (Gupta, et al 1999). On these normalization methods, see Vasconcelos (2004) or Gupta andCampanha (1999, 2002).…”

Section: Iii2 New Empirical Datamentioning

confidence: 99%