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 Podolskijās group. Dr. Amorino earned her PhD in July 2020 under the supervision of Prof. Arnaud Gloter at LaMME, UniversitĆ© 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: UniversitƩ Paris-Saclay, France (LaMME)
Duration: October 2017 ā 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
Masterās Degree in Mathematics
Institution: UniversitĆ Statale di Milano, Italy
Duration: 2015 ā 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: UniversitƩ Paris VII Diderot, France
Program: Master M2MO: ModƩlisation AlƩatoire
Duration: September 2016 ā January 2017
Bachelorās Degree in Mathematics
Institution: UniversitĆ Statale di Milano, Italy
Duration: 2012 ā 2015
Professional Profiles
Google Scholar
ORCID Profile
Professional Experience
Assistant Professor
Universitat Pompeu Fabra, Barcelona, Spain
April 2024 ā 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 ā March 2024
Collaborated with Prof. Mark Podolskijās 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
UniversitƩ Paris-Saclay (LaMME Laboratory), France
September 2016 ā 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 ā[insert thesis title],ā 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.
Steinās 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,
C
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. PilipauskaitÄ, 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 JumpāDiffusion 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 Hƶlder 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 Hƶlder 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.