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Federico P. Cortese
Federico P. Cortese
National Research Council of Italy - Institute for Applied Mathematics and Information Technologies
Verified email at mi.imati.cnr.it - Homepage
Title
Cited by
Cited by
Year
What drives cryptocurrency returns? A sparse statistical jump model approach
FP Cortese, PN Kolm, E Lindström
Digital Finance 5 (3), 483-518, 2023
102023
Generalized information criteria for sparse statistical jump models
F Cortese, P Kolm, E Linstrom
Symposium i anvendt statistik-Copenhagen Business School, 68-78, 2023
92023
Tail dependence in financial markets: A dynamic copula approach
FP Cortese
Risks 7 (4), 116, 2019
72019
Generalized Information Criteria for High-Dimensional Sparse Statistical Jump Models
FP Cortese, PN Kolm, E Lindstrom
Available at SSRN 4774429, 2024
62024
Statistical jump model for mixed-type data with missing data imputation
FP Cortese, A Pievatolo
arXiv preprint arXiv:2409.01208, 2024
12024
Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring
FP Cortese, A Pievatolo
arXiv preprint arXiv:2411.09726, 2024
2024
Modelling and forecasting correlated failure
F Cortese, A Pievatolo
2nd Italian Conference on Economic Statistics (ICES 2024), Statistical …, 2024
2024
Statistical Modeling and Temporal Clustering of Multivariate Time-Series with Applications to Financial Data
F Cortese
Università degli Studi di Milano-Bicocca, 2024
2024
Investigating Time-Varying Correlations between Cryptocurrency and Financial Markets: A Gas-Based Approach
K LAWUOBAHSUMO, A Leccadito, FP Cortese, B Algieri
Available at SSRN 5007834, 2024
2024
Maximum Likelihood Estimation of Multivariate Regime Switching Student‐t Copula Models
FP Cortese, F Pennoni, F Bartolucci
International Statistical Review 92 (3), 327-354, 2024
2024
A Regime switching Student-t copula model for the analysis of cryptocurrencies data
F Cortese, F Bartolucci, F Pennoni
Book of Abstracts Mathematical and Statistical Methods for Actuarial …, 2022
2022
Hidden Markov and regime switching copula models for state allocation in multiple time-series
F Bartolucci, F Pennoni, FP Cortese
CLADAG, 36, 2021
2021
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