Assist. Prof. Dr. Chiara Amorino, Universitat Pompeu Fabra, Spain
Assist. Prof. Dr. Chiara Amorino is an Assistant Professor at Universitat Pompeu Fabra in Barcelona, a position she has held since April 2024. She previously served as a postdoctoral researcher at the University of Luxembourg in Prof. Mark Podolskij’s group. Dr. Amorino earned her PhD in July 2020 under the supervision of Prof. Arnaud Gloter at LaMME, Université Paris-Saclay. Her research focuses on statistical inference for stochastic differential equations, exploring topics such as thresholding methods, high-frequency data, Malliavin calculus, and volatility estimation. She is also passionate about McKean-Vlasov equations, Hawkes processes, and local differential privacy.
Education
PhD in Applied Mathematics
Institution: Université Paris-Saclay, France (LaMME)
Duration: October 2017 – August 2020
Thesis Title: “Bias correction for the drift and volatility estimation of a jump diffusion and nonparametric adaptive estimation of the invariant measure”
Supervisor: Prof. Arnaud Gloter
Jury Members: Alexandre Brouste (Rapporteur), Fabienne Comte, Arnaud Gloter, Agathe Guilloux, Eulalia Nualart (Rapporteur), Fabien Panloup, Mathieu Rosenbaum
Master’s Degree in Mathematics
Institution: Università Statale di Milano, Italy
Duration: 2015 – 2017
Dissertation Title: “Randomization method and backward differential stochastic equations for optimal control”
Supervisor: Prof. Marco Fuhrman
Grade: Magna cum laude (“110/110 e lode”, First-Class Honours)
Visiting Student
Institution: Université Paris VII Diderot, France
Program: Master M2MO: Modélisation Aléatoire
Duration: September 2016 – January 2017
Bachelor’s Degree in Mathematics
Institution: Università Statale di Milano, Italy
Duration: 2012 – 2015
Professional Profiles
Google Scholar
ORCID Profile
Professional Experience
Assistant Professor
Universitat Pompeu Fabra, Barcelona, Spain
April 2024 – Present
Currently engaged in teaching, research, and academic supervision at Universitat Pompeu Fabra.
Focused on advanced topics in [insert specific subject area, e.g., mathematics, statistics, etc.].
Contributing to departmental initiatives and collaborations.
Postdoctoral Researcher
University of Luxembourg
August 2020 – March 2024
Collaborated with Prof. Mark Podolskij’s group on [insert specific research focus, e.g., stochastic processes, probability theory, etc.].
Published in high-impact journals and presented findings at international conferences.
Contributed to the development of new methodologies and computational tools in the field.
PhD Researcher
Université Paris-Saclay (LaMME Laboratory), France
September 2016 – July 2020
Conducted doctoral research under the supervision of Prof. Arnaud Gloter.
Focused on [insert specific research area, e.g., stochastic analysis, mathematical modeling, etc.].
Defended a thesis titled “[insert thesis title],” which contributed novel insights to the field.
Collaborated with interdisciplinary teams and participated in academic teaching duties.
Research Interests
Jump Diffusion Processes: Analysis and applications of stochastic processes incorporating jumps, relevant in finance and other applied fields.
High Dimensional Statistics: Developing methodologies and theoretical insights for analyzing data with a large number of variables.
Volatility Estimation: Techniques for measuring and predicting volatility in financial and stochastic systems.
Limit Theorems: Study of asymptotic behaviors and distributional approximations in probability theory.
Malliavin Calculus: Applying stochastic calculus for variations in fields like quantitative finance and stochastic analysis.
Nonparametric Statistics: Developing flexible statistical methods without assuming strict parametric models.
Stein’s Method: A probabilistic technique for assessing distributional approximations.
McKean-Vlasov SDEs: Investigations into stochastic differential equations with mean-field interactions.
Local Differential Privacy: Researching privacy-preserving mechanisms in statistical analysis and data sharing.
Minimax Risk and Convergence Rates: Studying optimality and efficiency in statistical decision-making processes.
Fractional Brownian Motion: Exploring processes with long-range dependence and their applications.
Thresholding Methods: Statistical techniques for signal processing and data analysis.
Bayesian Statistics: Combining prior information with data for statistical inference and decision-making.
Technical Skills
LaTeX
Python
R
SAS
Matlab,
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Top Notable Publications
Contrast Function Estimation for the Drift Parameter of Ergodic Jump Diffusion Processes
Authors: C. Amorino, A. Gloter
Journal: Scandinavian Journal of Statistics
Year: 2020
Citations: 26
This paper addresses drift parameter estimation using contrast functions, offering insights into jump diffusion processes.
Parameter Estimation of Discretely Observed Interacting Particle Systems
Authors: C. Amorino, A. Heidari, V. Pilipauskaitė, M. Podolskij
Journal: Stochastic Processes and their Applications
Year: 2023
Citations: 22
A collaborative effort exploring parameter estimation in interacting particle systems observed at discrete intervals.
Unbiased Truncated Quadratic Variation for Volatility Estimation in Jump Diffusion Processes
Authors: C. Amorino, A. Gloter
Journal: Stochastic Processes and their Applications
Year: 2020
Citations: 20
Proposes a novel technique for volatility estimation using truncated quadratic variation.
Invariant Density Adaptive Estimation for Ergodic Jump–Diffusion Processes Over Anisotropic Classes
Authors: C. Amorino, A. Gloter
Journal: Journal of Statistical Planning and Inference
Year: 2021
Citations: 17
Focuses on invariant density estimation and its adaptive approach in anisotropic frameworks.
Optimal Convergence Rates for the Invariant Density Estimation of Jump-Diffusion Processes
Authors: C. Amorino, E. Nualart
Journal: arXiv preprint
Year: 2021
Citations: 9
Examines convergence rates for invariant density estimation in jump-diffusion contexts.
Rate of Estimation for the Stationary Distribution of Jump-Processes Over Anisotropic Hölder Classes
Author: C. Amorino
Journal: arXiv preprint
Year: 2020
Citations: 8
Discusses estimation rates for stationary distributions under specific anisotropic conditions.
Minimax Rate of Estimation for Invariant Densities Associated to Continuous Stochastic Differential Equations Over Anisotropic Hölder Classes
Authors: C. Amorino, A. Gloter
Journal: Scandinavian Journal of Statistics
Year: 2024
Citations: 7
A forthcoming study delving into minimax rates for invariant density estimations in stochastic differential equations.
On the Nonparametric Inference of Coefficients of Self-Exciting Jump-Diffusion
Authors: C. Amorino, C. Dion-Blanc, A. Gloter, S. Lemler
Journal: Electronic Journal of Statistics
Year: 2022
Citations: 6
Investigates nonparametric inference for coefficients in self-exciting jump-diffusion models.
Joint Estimation for Volatility and Drift Parameters of Ergodic Jump Diffusion Processes via Contrast Function
Authors: C. Amorino, A. Gloter
Journal: Statistical Inference for Stochastic Processes
Year: 2021
Citations: 6
Proposes methods for simultaneous estimation of volatility and drift parameters.
Estimation of the Invariant Density for Discretely Observed Diffusion Processes: Impact of the Sampling and the Asynchronicity
Authors: C. Amorino, A. Gloter
Journal: Statistics
Year: 2023
Citations: 5
Analyzes the effects of sampling and asynchronicity on invariant density estimation.