Fabrizio Lillo

8.7k total citations
169 papers, 4.8k citations indexed

About

Fabrizio Lillo is a scholar working on Economics and Econometrics, Finance and Statistical and Nonlinear Physics. According to data from OpenAlex, Fabrizio Lillo has authored 169 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Economics and Econometrics, 86 papers in Finance and 38 papers in Statistical and Nonlinear Physics. Recurrent topics in Fabrizio Lillo's work include Complex Systems and Time Series Analysis (102 papers), Financial Risk and Volatility Modeling (47 papers) and Financial Markets and Investment Strategies (46 papers). Fabrizio Lillo is often cited by papers focused on Complex Systems and Time Series Analysis (102 papers), Financial Risk and Volatility Modeling (47 papers) and Financial Markets and Investment Strategies (46 papers). Fabrizio Lillo collaborates with scholars based in Italy, United States and United Kingdom. Fabrizio Lillo's co-authors include Rosario N. Mantegna, J. Doyne Farmer, G. Bonanno, Michele Tumminello, Guido Caldarelli, Salvatore Miccichè, Massimiliano Zanin, Szabolcs Mike, Fulvio Corsi and László Gillemot and has published in prestigious journals such as Nature, Physical Review Letters and Bioinformatics.

In The Last Decade

Fabrizio Lillo

157 papers receiving 4.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Fabrizio Lillo Italy 35 3.4k 2.3k 1.3k 632 357 169 4.8k
Zhi‐Qiang Jiang China 30 2.9k 0.8× 1.4k 0.6× 1.2k 0.9× 320 0.5× 186 0.5× 78 3.5k
Boris Podobnik Croatia 34 4.7k 1.4× 2.1k 0.9× 2.5k 1.9× 465 0.7× 171 0.5× 112 6.6k
Benjamin Miranda Tabak Brazil 42 4.7k 1.4× 3.6k 1.6× 1.0k 0.8× 1.0k 1.6× 843 2.4× 252 6.3k
Vasiliki Plerou United States 32 5.1k 1.5× 3.0k 1.3× 2.0k 1.5× 553 0.9× 169 0.5× 51 6.0k
Parameswaran Gopikrishnan United States 31 5.8k 1.7× 3.4k 1.5× 2.3k 1.7× 592 0.9× 183 0.5× 47 6.7k
Ladislav Krištoufek Czechia 36 4.8k 1.4× 1.8k 0.8× 454 0.3× 535 0.8× 385 1.1× 110 5.8k
Rama Cont United Kingdom 37 3.7k 1.1× 4.9k 2.1× 466 0.4× 1.4k 2.3× 410 1.1× 138 6.6k
Neil Chriss United States 8 1.9k 0.6× 1.6k 0.7× 691 0.5× 478 0.8× 105 0.3× 16 3.0k
Daniel O. Cajueiro Brazil 29 2.5k 0.7× 2.0k 0.9× 683 0.5× 430 0.7× 372 1.0× 114 3.3k
Stelios Bekiros Italy 43 3.9k 1.2× 1.6k 0.7× 935 0.7× 1.0k 1.6× 941 2.6× 189 7.0k

Countries citing papers authored by Fabrizio Lillo

Since Specialization
Citations

This map shows the geographic impact of Fabrizio Lillo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fabrizio Lillo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabrizio Lillo more than expected).

Fields of papers citing papers by Fabrizio Lillo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fabrizio Lillo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fabrizio Lillo. The network helps show where Fabrizio Lillo may publish in the future.

Co-authorship network of co-authors of Fabrizio Lillo

This figure shows the co-authorship network connecting the top 25 collaborators of Fabrizio Lillo. A scholar is included among the top collaborators of Fabrizio Lillo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fabrizio Lillo. Fabrizio Lillo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lillo, Fabrizio, et al.. (2024). A machine learning approach to support decision in insider trading detection. EPJ Data Science. 13(1).
2.
Lillo, Fabrizio, et al.. (2024). The public use of early-stage scientific advances in carbon dioxide removal: a science-technology-policy-media perspective. Environmental Research Letters. 19(11). 114009–114009. 1 indexed citations
3.
Lillo, Fabrizio, et al.. (2023). Analysis of Bank Leverage via Dynamical Systems and Deep Neural Networks. SIAM Journal on Financial Mathematics. 14(2). 598–643.
4.
Lillo, Fabrizio, et al.. (2021). Betweenness centrality for temporal multiplexes. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 17 indexed citations
5.
Ferragina, Paolo, et al.. (2021). On the performance of learned data structures. Theoretical Computer Science. 871. 107–120. 12 indexed citations
6.
Delgado, Luis, et al.. (2021). Network-wide assessment of ATM mechanisms using an agent-based model. Journal of Air Transport Management. 95. 102108–102108. 4 indexed citations
7.
Lillo, Fabrizio, et al.. (2020). Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Use Siena air (University of Siena). 16 indexed citations
8.
Lillo, Fabrizio, et al.. (2020). Are trading invariants really invariant? Trading costs matter. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 6 indexed citations
9.
Ferragina, Paolo, et al.. (2020). Why are learned indexes so effective. CINECA IRIS Institutial research information system (University of Pisa). 1. 3123–3132. 2 indexed citations
10.
Lillo, Fabrizio, et al.. (2020). Unveiling the relation between herding and liquidity with trader lead-lag networks. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 9 indexed citations
11.
Buccheri, Giuseppe, Giacomo Bormetti, Fulvio Corsi, & Fabrizio Lillo. (2020). A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics. Journal of Business and Economic Statistics. 39(4). 920–936. 30 indexed citations
12.
Lillo, Fabrizio, et al.. (2019). Inference of the kinetic Ising model with heterogeneous missing data. Physical review. E. 99(6). 62138–62138. 8 indexed citations
13.
Barucca, Paolo, et al.. (2018). A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 33 indexed citations
14.
Lillo, Fabrizio, et al.. (2018). Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction. Cineca Institutional Research Information System (Tor Vergata University). 29 indexed citations
15.
Gatheral, Jim, et al.. (2016). Discrete homotopy analysis for optimal trading execution with nonlinear transient market impact. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 7 indexed citations
16.
Lillo, Fabrizio, et al.. (2016). Statistical characterization of deviations from planned flight trajectories in air traffic management. Journal of Air Transport Management. 58. 152–163. 17 indexed citations
17.
Lillo, Fabrizio, László Gillemot, & J. Doyne Farmer. (2011). There's more to volatility than volume. Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences). 29 indexed citations
18.
Pantaleo, Ester, Michele Tumminello, Fabrizio Lillo, & Rosario N. Mantegna. (2011). When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators. Scuola Normale Superiore di Pisa. 34 indexed citations
19.
Moro, Esteban, Luis G. Moyano, Austin Gerig, et al.. (2009). Market Impact and Trading Profile of Large Trading Orders in Stock Markets. SSRN Electronic Journal. 2 indexed citations
20.
Lillo, Fabrizio & Rosario N. Mantegna. (2001). Omori law after a financial market crash. arXiv (Cornell University). 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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