http://www.cnr.it/ontology/cnr/individuo/prodotto/ID223376

Bridging stylized facts in finance and data non-stationarities (Articolo in rivista)

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Bridging stylized facts in finance and data non-stationarities (Articolo in rivista) (literal)

Anno

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Alternative label

Sabrina Camargo (1); Sílvio M. Duarte Queirós (2); Celia Anteneodo (3) (2013)
Bridging stylized facts in finance and data non-stationarities
in The European physical journal. B, Condensed matter physics (Print)
(literal)

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Sabrina Camargo (1); Sílvio M. Duarte Queirós (2); Celia Anteneodo (3) (literal)

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(1) Department of Physics, PUC-Rio, Rua Marquês de São Vicente 225, Gávea, CEP 22453-900, Rio de Janeiro, RJ, Brazil (2) Istituto dei Sistemi Complessi - CNR, Via dei Taurini 19, 00185, Roma, Italy (3) Department of Physics, PUC-Rio and National Institute of Science and Technology for Complex Systems, Rua Marquês de São Vicente 225, Gávea, CEP 22453-900, Rio de Janeiro, RJ, Brazil (literal)

Titolo

Abstract

Employing a recent technique which allows the representation of nonstationary data by means of a juxtaposition of locally stationary paths of different length, we introduce a comprehensive analysis of the key observables in a financial market: the trading volume and the price fluctuations. From the segmentation procedure we are able to introduce a quantitative description of statistical features of these two quantities, which are often named stylized facts, namely the tails of the distribution of trading volume and price fluctuations and a dynamics compatible with the U-shaped profile of the volume in a trading section and the slow decay of the autocorrelation function. The segmentation of the trading volume series provides evidence of slow evolution of the fluctuating parameters of each patch, pointing to the mixing scenario. Assuming that long-term features are the outcome of a statistical mixture of simple local forms, we test and compare different probability density functions to provide the long-term distribution of the trading volume, concluding that the log-normal gives the best agreement with the empirical distribution. Moreover, the segmentation of the magnitude price fluctuations are quite different from the results for the trading volume, indicating that changes in the statistics of price fluctuations occur at a faster scale than in the case of trading volume. (literal)

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