About Me

I am a Ph. D. Canditate 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).

My research focuses on Macro-Finance, Monetary Policy and Large Language Models (LLMs) in Financial Economics.

I am on the Academic Job Market in November 2025.

References: Max Croce, Carlo A. Favero, Claudio Tebaldi

Job Market Paper NEW!

• Monetary Policy Shocks: A New Hope
  Large Language Models and Central Bank Communication

  I develop a multi-agent LLM framework that processes Federal Reserve communications to construct narrative monetary policy surprises. By analyzing Beige Books and Minutes released before each FOMC meeting, the system generates conditional expectations that yield less noisy surprises than market-based measures. These surprises produce theoretically consistent impulse responses where contractionary shocks generate persistent disinflationary effects, and enable profitable yield curve trading strategies that outperform alternatives. By directly extracting expectations rather than cleaning surprises ex post, this approach demonstrates how multi-agent LLMs can implement narrative identification at scale without contamination in high-frequency measures.

  [Manuscript]

Job Market Dashboard NEW!

Ivan Shchapov and I used LLMs to classify postings' text across: Fields (Macro, Micro, Metrics, Finance), institution types (R1s, LACs, Policy, Private Sector), and position types (AP, Postdoc, Lecturer). Through Oct 12: 2025-2026 has 451 postings vs. 666 in 2024-2025

Research

Working Papers

• Scoring in the Transition — with G. Bezzi, Max Croce, and G. Gigante

  We propose a novel methodology to assign scores in a model in which: (i) a firm has a valuable environmental growth option whose payoff is realized at the end of a transition period, (ii) the firm's CEO has private information on the growth option quality; and (iii) a fund manager must decide how much to invest in this firm under limited information. In this setting, a progress-oriented score improves the allocation of capital. In an application, we show that assessing a CEO's decarbonization plan helps the fund manager to disentangle good-quality (green) growth options from bad-quality (brown) growth options. In the data, an investment strategy that uses progress-based scores delivers a superior performance.

• The Scope of Scope 3 — with Max Croce, Nicolás Guíñez, Alejandra Inzunza-Méndez and Claudio Tebaldi

  We propose a novel network-based methodology in order to measure emissions along complex international supply chains (SC). Using international data for 62,700 firms, we characterize SC-level emissions across countries and industries. Our methodology enables us to run counterfactual experiments and formulate granular forecasts about high-scope emissions with respect to many different scenarios such as maritime disruption, military conflicts, trade wars, revised carbon taxes.

• Monetary Policy in the COVID Era and Beyond — 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]

Work in Progress

• Green DAOs for Brown Networks — with Max Croce, Nicolás Guíñez, Alejandra Inzunza-Méndez and Claudio Tebaldi

  We propose a novel model to think about networks with emission externalities. We consider a decentralized economy in which (i) emissions are not priced; and (ii) firms are subject to firm-specific borrowing constraints depending on their level of greenness. Greenness is measured by either looking at scope-1, scope-3 and embedded emissions. We show that this economy is highly inefficient. We then look at an economy in which: (i) a DAO borrows on behalf of the entire network allocates capital internally; (ii) all producers belonging to that DAO join a blockchain recording transaction of goods and emissions. We show that this setting replicates the first-best. Preliminary empirical results suggest that the welfare gains produced by a Green-DAO could be significant.

Published Papers

• Towards Data-Congruent Models of the Term Structure of Interest Rates — with Carlo A. Favero
  Econometric Reviews, 2025, 1–23

  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]

Policy Papers

• The Conduct of ECB Monetary Policy Under International Uncertainty — with L. Bottazzi, C. Favero, F. Giavazzi, T. Monacelli, V. Guerrieri and G. Lorenzoni
  European Parliament - Monetary Dialogue

  This paper examines ECB monetary policy amid rising international uncertainty. We focus on three global risks: renewed trade protectionism, euro appreciation, and US fiscal fragility. Using inflation forecasts and survey data, we evaluate the ECB's evolving policy framework. A potential US fiscal crisis poses risks but also creates an opportunity for Europe to supply a global safe asset. We argue that a European Debt Agency issuing common debt could mitigate contagion and enhance Europe's financial sovereignty. 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 23 June 2025.

  [PDF] [European Parliament Link]
• 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 and lecturer since the course 2022/2023.

Teaching Evaluations (2024/2025)

Courses a.y. 2025/2026

• 40215 FINANCE 2 (Teaching, PhD Course)

Courses a.y. 2024/2025

• 20136 ADVANCED MATHEMATICS FOR ECONOMICS AND SOCIAL SCIENCES (TA)
• 30560 MATHEMATICAL MODELLING FOR FINANCE (Teaching, Master Course)
• 40215 FINANCE 2 (Teaching, PhD Course)

Courses a.y. 2023/2024

• 20135 TEORIA DELLA FINANZA / THEORY OF FINANCE (TA)
• 20136 ADVANCED MATHEMATICS FOR ECONOMICS AND SOCIAL SCIENCES (TA)
• 20897 ADVANCED CORPORATE FINANCE FOR MANAGEMENT (TA)
• 30268 MATEMATICA GENERALE E FINANZIARIA / MATHEMATICS (TA)
• 30454 LOGIC AND METHODOLOGY OF SOCIAL SCIENCES (TA)
• 30560 MATHEMATICAL MODELLING FOR FINANCE (TA)

Courses a.y. 2022/2023

• 20211 FINANCIAL MANAGEMENT E CORPORATE BANKING / FINANCIAL MANAGEMENT AND CORPORATE BANKING (TA)
• 30454 LOGIC AND METHODOLOGY OF SOCIAL SCIENCES (TA)
• 30560 MATHEMATICAL MODELLING FOR FINANCE (TA)

Resources

Here you can find some useful resources and code repositories:

• Fama-French Data Downloader and Cleaner

  This project provides a set of scripts to download, clean, and manage data from the Fama-French data library.

  • List and download all or specific datasets from the Fama-French website.
  • Process raw data files to handle formatting inconsistencies.
  • Save clean, analysis-ready data.
• 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.

• arXiv MCP Server

  A Model Context Protocol (MCP) server that provides seamless access to arXiv's extensive collection of academic papers. This tool enables AI assistants to search, retrieve, analyze, and manage research papers directly from arXiv, featuring:

  • Smart search with author name variations and category filtering
  • Full-text extraction with adaptive PDF processing (3-tier system)
  • Citation network analysis via Semantic Scholar integration
  • Local library management with collections and tagging
  • Paper summarization and key findings extraction
  • Multi-format bibliography export (APA, MLA, Chicago, BibTeX)

  Perfect for researchers and students who want to integrate arXiv paper analysis into their AI workflows, enabling quick literature reviews, citation tracking, and research paper management.

• Mistral OCR CLI

  A command-line interface tool for OCR (Optical Character Recognition) using Mistral AI's vision capabilities. This tool provides efficient extraction of text from images and PDFs, making it easy to digitize documents, extract data from scanned papers, and convert visual information into machine-readable text. Features include:

  • High-accuracy text extraction from images and scanned documents
  • Support for multiple image formats and PDF files
  • Batch processing capabilities for multiple files
  • Integration with Mistral AI's advanced vision models
  • Command-line interface for easy automation and scripting
  • Configurable output formats for different use cases

  Ideal for researchers and developers who need to extract text from academic papers, historical documents, or any visual content, streamlining the digitization workflow and making content searchable and analyzable.

Contact

fernandezfuertesruben@gmail.com