About Me

I'm PhD student in Finance at Bocconi University. My background is in Mathematics (Bachelor at the Universidad Autonoma de Madrid) and Mathematical Finance (MSc at the University of Manchester). I have been working in Macro-Finance (specifically in Monetary Policy) and Fin-Tech applications to the Supply Chain. I'm also interested in Large Language Models (LLMs) and their applications to Financial Economics. I am pretty open to collaborations, discussions and critical feedback.

Research

Published Papers

  • Towards Data-Congruent Models of the Term Structure of Interest Rates — with Carlo A. Favero Econometric Reviews

    Bond yields can be decomposed into two unobservable components: the expected sequence of short-term rates and term premia. The identification of these two components is crucial to understand bond pricing and the effect of monetary policy on the term structure of interest rates. This paper illustrates how M.H. Pesaran's prescription of congruency between the salient features of the data and the reduced form, explicitly derived from stochastic dynamic optimization, effectively facilitates the relevant decomposition. By examining the historical evolution of term structure models, we demonstrate that the chosen specifications have not consistently aligned with the data, presenting a missed opportunity. In fact, a data-congruent specification helps in improving forecasts of the dynamics of US short-term rates and generates stationary dynamics for the term premia.

    [Manuscript] [Online Appendix] [Published Version]

Working Papers

  • Monetary Policy in the COVID Era and Beyond: The Fed vs the ECB — with Carlo A. Favero

    This study examines monetary policy during and post-COVID by analysing innovative rules based on data from before the pandemic. It models fluctuating monetary policy rates using a stochastic trend, linking potential output growth, demographic age distribution, and inflation expectations to the prevailing interest rate trends in both the US and the Eurozone. The cyclical variations in short-term rates are associated with monetary policy through the conventional Taylor rule indicators. Whilst the standard model is robust for the US both in and out of sample, the Eurozone displays less consistent in-sample results and marked deviations in out-of-sample tests. Addressing the ECB's concerns about bond market fragmentation doesn't yield better results. Instead, a model in which the ECB follows the US example with caution and delay proves more effective.

    [Manuscript]
  • Green DAOs for Brown Networks

    Draft coming soon!

  • A New Hope: LLMs and Monetary Policy Shocks

    Draft coming soon!

Policy Papers

  • Euro Area Risks Amid US Protectionism — with L. Bottazzi, C. Favero, F. Giavazzi, V. Guerrieri, G. Lorenzoni and T. Monacelli European Parliament - Monetary Dialogue

    Were the US to impose large and lasting tariffs on its imports from the EU, the effect on the euro area (EA) would be substantial and far-reaching. We expect the direct impact to be inflationary in the US and contractionary on EA aggregate demand and output. The indirect impact through an appreciation of the dollar (partly already occurred) tends to transfer inflation from the US to Europe. The ECB should be mindful that both deflationary and inflationary influences may ensue, and be ready to adjust monetary policy promptly if necessary to maintain price stability. This document was provided by the Economic Governance and EMU Scrutiny Unit at the request of the Committee on Economic and Monetary Affairs (ECON) ahead of the Monetary Dialogue with the ECB President on 20 March 2025.

    [PDF] [European Parliament Link]

Teaching

I have been a teaching assistant since the course 2022/2023. This year I will be lecturer as well. Below you can find the detailed information:

Courses a.y. 2024/2025

Courses a.y. 2023/2024

Courses a.y. 2022/2023

Resources

Here you can find some useful resources and code repositories:

  • ConsensusEconomics Data Handler

    IMPORTANT DISCLAIMER: This repository requires a valid Consensus Economics subscription. Access to Consensus Economics' proprietary database is a prerequisite for utilizing this code. This repository does not contain or share any Consensus Economics data; it solely provides tools and methods for processing and analyzing the data once you have legitimate access to it through a valid subscription.

    A collection of tools and utilities designed to efficiently handle and process Consensus Economics datasets. This repository includes functions for data cleaning, transformation, and analysis, specifically tailored for working with Consensus Economics' file formats and data structures.

  • Macroeconomic Data Handler

    A continuously updated Python package for fetching, analyzing, and storing macroeconomic data. Currently integrates data from Federal Reserve Economic Data (FRED) and Federal Reserve Green Book Projections, with AWS integration and LLM capabilities via Groq. Features include:

    • Automated retrieval of key economic indicators from FRED API using LLM capabilities via Groq to find the appropriate code for the variable the researcher is asking for
    • Historical Federal Reserve Green Book Projections data extraction and analysis
    • AWS Integration for secure storage and management
    • Interactive command-line interface for custom data queries
    • Tracking of essential metrics (GDP, Industrial Production, Unemployment, CPI, etc.)
  • Term Structure Models Implementation (Classical vs Mine)

    This repository implements the model developed in the forthcoming Econometric Reviews paper "Towards Data-Congruent Models of the Term Structure of Interest Rates" (coauthored with Carlo A. Favero). It provides a comprehensive implementation of term structure models for analyzing yield curves and macroeconomic factors, focusing on:

    • Implementation of ACM (Adrian, Crump, Moench) and FF (Favero, Fernández-Fuertes) models
    • Data-congruent term structure modeling with macroeconomic factors
    • Common trend component analysis
    • Out-of-sample forecasting comparisons
    • Visualization tools for yield curves, model parameters, and returns
  • Stock-Watson (2010) GDP Interpolation

    This package implements the Stock-Watson (2010) procedure for temporal disaggregation of quarterly GDP to monthly values. It provides tools to interpolate quarterly economic data to monthly frequency using various methods:

    • Cubic spline and linear interpolation methods
    • Stock-Watson (2010) Kalman filter interpolation method
    • Comparison visualizations between quarterly and monthly data
    • Configurable via YAML configuration files
    • Comprehensive data validation and progress tracking

    The implementation ensures that the sum of three monthly values equals the quarterly value and uses related monthly indicators to guide the interpolation through a state-space model and Kalman filter for optimal results.

  • VarKit: Python Implementation of VAR-Toolbox

    A Python implementation of Ambrogio Cesa-Bianchi's VAR-Toolbox, originally written in MATLAB. This package provides a modern, Pythonic approach to Vector Autoregressive (VAR) analysis, featuring:

    Based on the original VAR-Toolbox by Ambrogio Cesa-Bianchi, this Python implementation aims to make VAR analysis more accessible to the Python-based research community while maintaining the robustness and functionality of the original MATLAB version.

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