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SUMMARY:Financial return distributions across markets and time scales
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UID:indico-contribution-17-201@indico.fis.agh.edu.pl
DESCRIPTION:Speakers: Stanisław Drożdż (Institute of Nuclear Physics Po
lish Academy of Sciences and Cracow University of Technology\, Poland)\nTh
e dynamics of price changes involves very complex processes and constitute
s one of the central issues in Econophysics. The functional forms of retur
n distributions considered and reported in the literature include the Levy
distribution and its truncated variant\, power-laws and\, in particular\,
its inverse-cubic case\, the q-Gaussians and the stretched exponentials.
These may vary among the financial instruments and even for the same instr
ument typically change with the time scale of aggregation. The present con
tribution is an attempt to provide a unified view on the related effects f
or different world markets\, also from the historical perspective. Special
focus is put on those quantitative characteristics of the return distibut
ions that are common to all the markets.\n\nhttps://indico.fis.agh.edu.pl/
event/69/contributions/201/
LOCATION:ONLINE
URL:https://indico.fis.agh.edu.pl/event/69/contributions/201/
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SUMMARY:Impact of multilayer topology on source localization in complex ne
tworks
DTSTART;VALUE=DATE-TIME:20210701T114000Z
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UID:indico-contribution-17-220@indico.fis.agh.edu.pl
DESCRIPTION:Speakers: Robert Paluch (Warsaw University of Technology)\nIt
is common nowadays to have to deal with information spreading on multilaye
r networks and often identification of the origin of said propagation can
be a crucial task. We examine the issue of locating the source of Suscepti
ble-Infected spreading process in a multilayer network using the Bayesian
inference and the maximum likelihood method established for general networ
ks and adapted here to cover multilayer topology. We show how the quality
of source identification depends on the network and spreading parameters a
nd find the existence of two-parameter ranges with different behavior. If
cross-layer spreading rate $\\beta_C$ is low\, observations in different l
ayers interfere\, lowering precision below that of relying on single layer
observers only. On the other hand\, if $\\beta_C$ is high observations sy
nergize\, raising accuracy above the level of a single-layer network of th
e same size and observer density. We also show a heuristic method to deter
mine in which mode is a system and therefore potentially improving the qua
lity of source localization by rejecting interfering observations.\n\nhttp
s://indico.fis.agh.edu.pl/event/69/contributions/220/
LOCATION:ONLINE
URL:https://indico.fis.agh.edu.pl/event/69/contributions/220/
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SUMMARY:Origin of multiscaling in finance and robust and statistically sig
nificant estimators
DTSTART;VALUE=DATE-TIME:20210701T104000Z
DTEND;VALUE=DATE-TIME:20210701T112000Z
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UID:indico-contribution-17-195@indico.fis.agh.edu.pl
DESCRIPTION:Speakers: Tiziana Di Matteo (Department of Mathematics - King'
s College London)\nThe multiscaling behaviour of financial time-series is
one of the acknowledged stylized facts in the literature [1]. The source o
f the measured multifractality in financial markets has been long debated
[2\,3]. In this talk I will discuss the origin of multiscaling in financia
l time-series\, investigate how to best quantify it [4\,5] and I will intr
oduce a new methodology that provides a robust estimation and tests the mu
lti-scaling property in a statistically significant way [6].\nI will show
results on the application of the Generalized Hurst exponent tool to diffe
rent financial time-series\, and I will show the powerfulness of such tool
to detect changes in markets’ behaviours\, to differentiate markets acc
ordingly to their degree of development\, to asses risk and to provide a n
ew tool for forecasting [7]. I will also show an empirical relationship\,
to our knowledge the first on in the literature\, which links a univariate
property\, i.e. the degree of multiscaling behaviour of a time series\, t
o a multivariate one\, i.e. the average correlation of the stock log-retur
ns with the other stocks traded in the same market and discuss its implica
tions [8]. \n\n[1] T. Di Matteo\, Q. Finance 7 (2007) 21\n[2] J. W Kantelh
ardt et al\, Physica A 316 (2002) 87\n[3] J. Barunik\, T. Aste\, T. Di Mat
teo\, R. Liu\, Physica A 391 (2012) 4234\n[4] R. J. Buonocore\, T. Aste\,
T. Di Matteo\, Chaos\, Solitons and Fractals 88 (2016) 38\n[5] R. J. Buono
core\, T. Di Matteo\, T. Aste\, Phys. Rev. E 95 (2017) 042311\n[6] G. Bran
di\, T. Di Matteo\, Eur. J. Finance (2021) DOI: 10.1080/1351847X.2021.1908
391\n[7] I. P. Antoniades\, G. Brandi\, L. G. Magafas\, T. Di Matteo\, Phy
sica A 565 (2021) 12556\n[8] R. J. Buonocore\, G. Brandi\, R. N. Mantegna\
, T. Di Matteo\, Q. Finance 20 (2020) 133\n\nhttps://indico.fis.agh.edu.pl
/event/69/contributions/195/
LOCATION:ONLINE
URL:https://indico.fis.agh.edu.pl/event/69/contributions/195/
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SUMMARY:Evolving network analysis of S&P500 components. Covid19 influence
on cross-correlation network structure.
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UID:indico-contribution-17-223@indico.fis.agh.edu.pl
DESCRIPTION:Speakers: Janusz Miśkiewicz (Uniwersytet Wrocławski\, Uniwer
sytet Przyrodniczy we Wrocławiu)\nInteraction is the basic feature of eco
nomic systems. Although it is possible to imagine a primitive self-sustain
ed tribe\, in the case of a developed economy the interaction (in positive
and negative sense i.e. cooperation and competition) are a crucial factor
of development. Those interactions are influenced by some events or the s
tate of the systems. The special cases are global events such as crises or
recently the Covid19 pandemic. In the present work changes in the correla
tion structure of economic interactions are investigated\, particularly th
e economy network features. The easiest achievable and reliable characteri
stic of the company is its value\, particularly in the case of the compani
es quoted on a stokes market. The study analyses evolution of the network
of companies quoted on New York stokes components being the components of
the S&P500 index. In the analysis\, the returns of daily logarithmic retur
ns are considered. In the analysis\, the distance matrices are calculated
using a sliding time window for the chosen set of time windows sizes (i.e.
5\, 10 and 20 days) and for each of the distance matrix the corresponding
network is constructed assuming that the companies with the distance with
in the interquartile range of the sets of the distances on the matrix are
connected on the network. The constructions algorithm remove extreme conne
ctions remaining the typical ones. Finally\, the following network paramet
ers are discussed: node rank entropy\, cycle entropy\, clustering coeffici
ent and transitivity coefficient. Particular interesting are changes trigg
ered by the recent events – the significant changes observed in entropy
time series indicated structural changes in the network of correlation str
ucture induced by the pandemic.\n\nhttps://indico.fis.agh.edu.pl/event/69/
contributions/223/
LOCATION:ONLINE
URL:https://indico.fis.agh.edu.pl/event/69/contributions/223/
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