PhD in Finance (MRes 2 Years + PhD), graduation expected in 2028.

Grade: 110/110 cum laude;

Relevant modules: Statistical Methods for Risk Analysis, Stochastic Models for Finance, Econometrics, Risk Measurement, Non Linear Models and Financial Econometrics, Stochastic Calculus for Finance, Computational Finance Lab (Matlab),  Commodity Markets, Derivatives and Insurance, Behavioural Finance, International Financial Regulation, Financial Economics;

Thesis: “Random Projection Techniques for Dimensionality Reduction in Time Series Analysis”. 

Grade: 110/110 cum laude;

Relevant modules: Macroeconomics, Microeconomics, Economic Policy, Public Finance, Portfolio Theory and Derivatives, Monetary Economics, Mathematics for Economic Sciences, Financial Mathematics, Financial Analysis, Corporate Finance.

Senior Research Analyst in the Research Hub of the Bank of England.

Research: “Credit Worthy: do Climate Change Risks matter for Sovereign Credit Ratings?”  (joint with Cappiello, L. (ECB), Ferrucci, G. (IMF) and Maddaloni, A. (ECB)).  

Research Assistance: anonymization of confidential datasets, cleaning and analysis of datasets (among the others: financial statements for bank and insurance companies, prices for goods, services and financial instruments, credit ratings, climate data, GHG emissions, energy data, surveys, births data, Climate-BES data, granular data on UK mortgages). 

Support to Research Activities

External Consultant in the Directorate General Research (DG-R) Financial Research Division (FIR) at the European Central Bank.

Project: Credit Worthy: do Climate Change Risks matter for Sovereign Credit Ratings?. Joint work with L. Cappiello, G. Ferrucci and A. Maddaloni.

Part time research assistant for the project “Machine learning and Bayesian learning methods for ESG Ratings”

Project: Learning From Experts. Energy Efficiency in Residential Buildings. Joint work with M. Billio, R. Casarin, M. Costola.

Trainee in Directorate General Research (DG-R) Financial Research Division (FIR) at the European Central Bank.

Research Assistance for research projects in the division.

Relevant projects: Maintenance of CISS indicator; Price Discovery in Financial Markets; Financial Condition Indices and their determinants; Impact of Climate Change on Corporate Credit Ratings. 

Summer School: “High-Dimensional Time Series Models I: Factor Models” (5 days);

Instructors: Luca Sala (theory) and Konstantin Boss (practice).

Topics Covered: Factor Models (principal components estimator; small N iid and dynamic, the EM algorithm, Kalman filter/smoother; large N iid and dynamic, consistency at large), Structural Factor Model (specification and estimation; Tools: impulse response functions, variance decomposition, historical decomposition; identification: short and long-run zero, sign restrictions, penalty function approach; DSGE and Factor models), Factor Augmented VAR (testing non-invertibility)

Applications: FM: Commonality in European regions, new Eurocoin, monetary policy in real time, nowcasting, measuring macroeconomic uncertainty. SFM: Monetary policy shocks, house prices, disaggregated prices. FAVAR: Monetary Policy, news shocks.

Training School: “The Macroeconomics of Climate Change” (3 days);

Instructors: Per Krusell and John Hassler.

Topics Covered: the greenhouse effect, the global energy budget, the relation between emissions and climate change, damages caused by climate change; economics of natural resources, directed endogenous technical change, integrated assessment models, climate policy evaluation; taxes vs cap and trade, the Paris agreement, EU ETS, the EU commissions proposal for net zero by 2050, need for complementary policies, transition risks, financial stability and stranded assets.

Summer School: “Bayesian Multivariate Models and Forecasting in Economics and Finance” (40 hours);

Instructors: Roberto Casarin, Matteo Ciccarelli, Francesco Ravazzolo.

The course covered state-of-the-art techniques and recent developments in Bayesian Multivariate Models, for structural analysis and forecasting, nonparametric methods and forecast combinations with a broad range of applications in economics and finance.

Basic and Advanced Matlab courses (56 hours);

Modules: Introduction to Matlab, Scientific Calculus, Big Data analysis, Parallel Computing, Machine Learning and Deep Learning.