Nonextensive statistical features of the Polish stock market fluctuations

Rak, Rafał; Dróżdż, S.; Kwapień, Jarosław

doi:10.1016/j.physa.2006.07.035

Cited by 58 publications

(69 citation statements)

References 26 publications

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“…This means that contemporary market dynamics significantly differs from the one observed 20 or even 10 years ago and described by the inverse cubic power law [3]. That these changes are a continuous process rather that a sudden transition we infer from the existence of intermediate stages in which the inverse cubic scaling was observed up to medium time scales of tens of minutes but was absent in daily data [9,11]. This effect suggests a scenario of constantly increasing market efficiency due to an acceleration of information processing in the world markets [9,16].…”

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confidence: 64%

“…for the German index DAX and for the Polish index WIG20, respectively, for the same period of time. While the returns of DAX did not exactly comply with the inverse cubic scaling also in the period 1998-99 [9], the ones of WIG20 indeed used to display this kind of behaviour in the past as documented in [11]. However, nowadays WIG20 also develops much thinner tails with α > 4 for ∆t = 1 min (Table 1).…”

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confidence: 94%

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Stock market return distributions: From past to present

Dróżdż

Forczek

Kwapień

et al. 2007

Physica A: Statistical Mechanics and its Applications

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We show that recent stock market fluctuations are characterized by the cumulative distributions whose tails on short, minute time scales exhibit power scaling with the scaling index α > 3 and this index tends to increase quickly with decreasing sampling frequency. Our study is based on high-frequency recordings of the S&P500, DAX and WIG20 indices over the interval The so-called financial stylized facts are among the central issues of econophysics research. Much effort has been devoted on both the empirical and the theoretical level to such phenomena like fat-tailed distributions of financial fluctuations, persistent correlations in volatility, multifractal properties of returns etc. Specifically, the interest in the return distributions can be traced back to an early work of Mandelbrot [1] in which he proposed a Lévy process as the one governing the logarithmic price fluctuations. Much later this issue was revisited in [2] based on data with much better statistics and a new model of exponentially-truncated Lévy flights was introduced. Then, in an extensive systematic study of the largest American stock markets [3] the distribution tails for both the prices and the indices were shown to be power-law with the scaling exponent α ≃ 3. The most striking outcome of that study was that despite the fact that the tails were well outside the Lévy-stable regime (α ≤ 2), they were apparently stable under time aggregation up to several days for indices and up to a month for stocks. The existence of return distributions with

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confidence: 64%

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confidence: 94%

Stock market return distributions: From past to present

Dróżdż

Forczek

Kwapień

et al. 2007

Physica A: Statistical Mechanics and its Applications

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“…Our results show that the Polish stock market can basically be expressed by a one--factor model with the fully developed couplings to occur at timescales longer than half a trading day. Since these properties are characteristic of small and emerging markets and since, on the other hand, the Polish market reveals some features that are common to well-developed markets (q-Gaussian structure of the returns probability density functions (p.d.f.s) [10], multifractality [11]), we arrive at the conclusion that at present WSE is in a transition phase from being an emerging market to becoming a fully-established one. Our analysis also proved that the strength of correlations among stocks crucially depends on their capitalizationthis effect is universal for all the markets investigated so far in literature.…”

Section: Discussionmentioning

confidence: 84%

“…We address the question whether the correlation properties of the Warsaw stock market still situate it among the emerging markets or, conversely, it has already matured enough to be considered a developed market. An inspiration for rising this question is the fact that the WSE evolution shares some properties (like the broad multifractal spectra and the returns distributions which can be fitted by the q-Gaussians) with the well-established markets, as our earlier studies showed [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Cross-Correlations in Warsaw Stock Exchange

et al. 2008

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We study the inter-stock correlations for the largest companies listed on Warsaw Stock Exchange and included in the WIG20 index. Our results from the correlation matrix analysis indicate that the Polish stock market can be well described by a one-factor model. We also show that the stock-stock correlations tend to increase with the timescale of returns and they approach a saturation level for the timescales of at least 200 min, i.e. an order of magnitude longer than in the case of some developed markets. We also show that the strength of correlations among the stocks crucially depends on their capitalization. These results combined with our earlier findings together suggest that now the Polish stock market situates itself somewhere between an emerging market phase and a mature market phase.

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“…The most extreme slope tails are observed for temperature T1 and T2 (β value is respectively 6 and 3). Interestingly, β = 3 and β = 4 is a typical value observed among others in the high frequency financial data [11,21,26]. In order to avoid divergent moments due to fat tails in the distribution of the fluctuations, we restrict q to −4, 4 throughout this paper.…”

Section: Resultsmentioning

confidence: 97%

Quantitative Characteristics of Correlations of Meteorological Data

Rak

Bwanakare

2016

Acta Phys. Pol. A

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This paper presents the quantitative characteristics of correlations (and cross-correlations) of plant main ecofactors i.e. the ground and over-ground temperature, the wind speed, and the humidity. The study is based upon hourly data statistical observations collected in the region of Lublin, in Poland for the period 2001.05. 07-2009.04.10. This paper indicates that plant growth conditions constitute an emergent response to the above direct eco-factors. Then, the dynamics properties of each eco-factor is first analyzed alone for its multifractal structure. We apply the multifractal detrended correlation analysis and multifractal detrended cross-correlation analysis. We show that the widest multifractal spectrum is for over-ground temperature and the strongest power-law cross-correlations exist between ground and over-ground temperature. Next, an impulse response analysis is carried out to measure dynamical inter causalities within all the considered variables. As far as cross-impact between different eco-variables is concerned, one observes that the wind speed, the ground temperature and the air humidity dynamics are the most influenced, in terms of memory length time, by external temperature.

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Nonextensive statistical features of the Polish stock market fluctuations

Cited by 58 publications

References 26 publications

Stock market return distributions: From past to present

Stock market return distributions: From past to present

Cross-Correlations in Warsaw Stock Exchange

Quantitative Characteristics of Correlations of Meteorological Data

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