Zobrazeno 1 - 10
of 451
pro vyhledávání: '"Mazzarisi, A."'
Autor:
Macchiati, Valentina, Marchese, Emiliano, Mazzarisi, Piero, Garlaschelli, Diego, Squartini, Tiziano
The level of systemic risk in economic and financial systems is strongly determined by the structure of the underlying networks of interdependent entities that can propagate shocks and stresses. Since changes in network structure imply changes in ris
Externí odkaz:
http://arxiv.org/abs/2409.03349
Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the time evolution of the system and are crucial for understanding transitio
Externí odkaz:
http://arxiv.org/abs/2407.16376
In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR's limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by pr
Externí odkaz:
http://arxiv.org/abs/2407.06619
Autor:
Mazzarisi, Onofrio, Smerlak, Matteo
Publikováno v:
Phys. Rev. E 110, 054403 Published 5 November 2024
Robert May famously used random matrix theory to predict that large, complex systems cannot admit stable fixed points. However, this general conclusion is not always supported by empirical observation: from cells to biomes, biological systems are lar
Externí odkaz:
http://arxiv.org/abs/2403.11014
Autor:
Ravagnani, Adele, Lillo, Fabrizio, Deriu, Paola, Mazzarisi, Piero, Medda, Francesca, Russo, Antonio
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveill
Externí odkaz:
http://arxiv.org/abs/2403.00707
Publikováno v:
Chaos Solitons & Fractals 186 (2024)
Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstr
Externí odkaz:
http://arxiv.org/abs/2402.11136
Autor:
Piero Mazzarisi, Adele Ravagnani, Paola Deriu, Fabrizio Lillo, Francesca Medda, Antonio Russo
Publikováno v:
EPJ Data Science, Vol 13, Iss 1, Pp 1-44 (2024)
Abstract Identifying market abuse activity from data on investors’ trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to suppo
Externí odkaz:
https://doaj.org/article/26aa39bf79f641ef94dd78c680f72386
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large ord
Externí odkaz:
http://arxiv.org/abs/2307.02375
Autor:
Mazzarisi, Piero, Ravagnani, Adele, Deriu, Paola, Lillo, Fabrizio, Medda, Francesca, Russo, Antonio
Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market s
Externí odkaz:
http://arxiv.org/abs/2212.05912
Autor:
Shternshis, Andrey, Mazzarisi, Piero
Shannon entropy is the most common metric to measure the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems may be in general non stationary, with an entropy value that is
Externí odkaz:
http://arxiv.org/abs/2211.05415