Statistical physics of complex systems in the world and in Lviv

Holovatch, Yu.; Dudka, M.; Blavatska, V.; Palchykov, Vasyl; Krasnytska, Mariana; Mryglod, Olesya

doi:10.30970/jps.22.2801

Cited by 5 publications

(1 citation statement)

References 75 publications

(110 reference statements)

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“…In recent decades, complex networks have become a promising area of research. It has applications in many disciplines: statistical physics (Holovatch, Dudka, Blavatska, Palchykov, & Mryglod, 2018), electrical engineering (Saleh, Esa, & Mohamed, 2018), biology (Roy, Grewal, & Roy, 2014), economics (Maluck, Glanemann, & Donner, 2018), climatology (Li, Liu, & Xu, 2017), ecology (Bai, Li, Li, Wang, & Xie, 2016) and sociology (Crossley, 2008). Applications of network theory also include logistic networks, the Internet, social networks and more.…”

Section: Introductionmentioning

confidence: 99%

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Liashenko

Kravets

Filogina

2020

ECO

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Financial markets are complex systems. Network analysis is an innovative method for improving data sharing and knowledge discovery in financial data. Oriented weighted networks were created for the Shanghai Composite, S&P500, DAX30, CAC40, Nikkei225, FTSE100, IBEX35 indexes, for CNY-JPY, EUR-USD, GBP-EUR, RUB-CNY and for cryptocurrency BTC-USD. We considered data since January 6, 2006 to September 6, 2019. The complex networks had a similar structure for both types of markets, which was divided into the central part (core) and the outer one (loops). The emergence of such a structure reflects the fact that, for the most part, the stock and currency markets develop around some significant state of volatility, but occasionally anomalies occur when the states of volatility deviate from the core. Comparing the topology of evolutionary networks and the differences found for the stock and currency markets networks, we can conclude that stock markets are characterized by a greater variety of volatility patterns than currency ones. At the same time, the cryptocurrency market network showed a special mechanism of volatility evolution compared to the currency and stock market networks.

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Section: Introductionmentioning

confidence: 99%

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Liashenko

Kravets

Filogina

2020

ECO

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Embedding technique and network analysis of scientific innovations emergence in an arXiv-based concept network

Brodiuk

Palchykov²,

Holovatch

2020

2020 IEEE Third International Conference on Data Stream Mining &Amp; Processing (DSMP)

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Novelty is an inherent part of innovations and discoveries. Such processes may be considered as an appearance of new ideas or as an emergence of atypical connections between the existing ones. The importance of such connections hints for investigation of innovations through network or graph representation in the space of ideas. In such representation, a graph node corresponds to the relevant concept (idea), whereas an edge between two nodes means that the corresponding concepts have been used in a common context. In this study we address the question about a possibility to identify the edges between existing concepts where the innovations may emerge. To this end, we use a well-documented scientific knowledge landscape of 1.2M arXiv.org manuscripts dated starting from April 2007 and until September 2019. We extract relevant concepts for them using the ScienceWISE.info platform. Combining approaches developed in complex networks science and graph embedding, we discuss the predictability of edges (links) on the scientific knowledge landscape where the innovations may appear.

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Physics Beyond Physics: Application of Physical Approaches in Quantitative Linguistics

Васильев¹,

Васильева²

2020

Ukr. J. Phys.

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The application of physical methods to solve non-physical problems has been considered. In particular, the prospects of physical approaches in quantitative linguistics are analyzed. The difference between the physical and non-physical methods is illustrated by an example of already existing “classical” models. A few mathematical models which make it possible to determine the rank-frequency dependence for words in a frequency dictionary, as well as the dependence of the dictionary volume on the text length, are proposed. It is shown that the physical approaches and principles that are used in physics can also be successfully applied to create mathematical models in linguistics.

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Statistical physics of complex systems in the world and in Lviv

Cited by 5 publications

References 75 publications

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Embedding technique and network analysis of scientific innovations emergence in an arXiv-based concept network

Physics Beyond Physics: Application of Physical Approaches in Quantitative Linguistics

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