33 citations · 65 across the 17 of their papers we have counts for
20 papers
Multilayer Analysis of the Global Trade Network
Chenyang Li, Leonardo Brogi, Andrea Civilini +3
Global trade is more than a single network of aggregate flows. Beneath the observable exchange of products among economies lies a complex multilayer structure, formed by thousands…
A high-frequency approach to Realized Risk Measures
Federico Gatta, Fabrizio Lillo, Piero Mazzarisi
We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Real…
Tackling estimation risk in Kelly investing using options
Fabrizio Lillo, Piero Mazzarisi, Ioanna-Yvonni Tsaknaki
The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. Howev…
Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
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 ti…
CAESar: Conditional Autoregressive Expected Shortfall
Federico Gatta, Fabrizio Lillo, Piero Mazzarisi
In financial risk management, Value at Risk (VaR) estimates potential portfolio losses but fails to account for losses beyond a certain threshold. Expected Shortfall (ES) addresses…
Dimensionality reduction techniques to support insider trading detection
Adele Ravagnani, Fabrizio Lillo, Paola Deriu +3
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…
Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods
Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
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 persistenc…
A machine learning approach to support decision in insider trading detection
Piero Mazzarisi, Adele Ravagnani, Paola Deriu +3
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 c…
Network-wide assessment of ATM mechanisms using an agent-based model
Luis Delgado, Gérald Gurtner, Piero Mazzarisi +4
This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting fo…
How Covid mobility restrictions modified the population of investors in Italian stock markets
Paola Deriu, Fabrizio Lillo, Piero Mazzarisi +3
This paper investigates how Covid mobility restrictions impacted the population of investors of the Italian stock market. The analysis tracks the trading activity of individual inv…
On the equivalence between the Kinetic Ising Model and discrete autoregressive processes
Carlo Campajola, Fabrizio Lillo, Piero Mazzarisi +1
Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity. In Statistical Physic…
Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages
Piero Mazzarisi, Silvia Zaoli, Carlo Campajola +1
Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether p…
Betweenness centrality for temporal multiplexes
Silvia Zaoli, Piero Mazzarisi, Fabrizio Lillo
Betweenness centrality quantifies the importance of a vertex for the information flow in a network. We propose a flexible definition of betweenness for temporal multiplexes, where…
New centrality and causality metrics assessing air traffic network interactions
Piero Mazzarisi, Silvia Zaoli, Fabrizio Lillo +2
In ATM systems, the massive number of interacting entities makes it difficult to identify critical elements and paths of disturbance propagation, as well as to predict the system-w…
Non-Markovian temporal networks with auto- and cross-correlated link dynamics
Oliver E. Williams, Piero Mazzarisi, Fabrizio Lillo +1
Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often non…
Trip Centrality: walking on a temporal multiplex with non-instantaneous link travel time
Silvia Zaoli, Piero Mazzarisi, Fabrizio Lillo
In complex networks, centrality metrics quantify the connectivity of nodes and identify the most important ones in the transmission of signals. In many real world networks, especia…
Detectability of Macroscopic Structures in Directed Asymmetric Stochastic Block Model
Mateusz Wilinski, Piero Mazzarisi, Daniele Tantari +1
We study the problem of identifying macroscopic structures in networks, characterizing the impact of introducing link directions on the detectability phase transition. To this end,…
When panic makes you blind: a chaotic route to systemic risk
Piero Mazzarisi, Fabrizio Lillo, Stefano Marmi
We present an analytical model to study the role of expectation feedbacks and overlapping portfolios on systemic stability of financial systems. Building on [Corsi et al., 2016], w…
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
Piero Mazzarisi, Paolo Barucca, Fabrizio Lillo +1
We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-sp…
Disentangling group and link persistence in Dynamic Stochastic Block models
Paolo Barucca, Fabrizio Lillo, Piero Mazzarisi +1
We study the inference of a model of dynamic networks in which both communities and links keep memory of previous network states. By considering maximum likelihood inference from s…