Research activities

QFinLab promotes research in quantitative finance, combining rigorous mathematical, statistical and computational methods with applications that are central to financial markets, risk management, regulation, innovation and sustainability. The Lab aims to be a reference point for academic research, advanced education, outreach and collaborations with institutions and industry.

Topics

The research activity of QFinLab covers core areas of quantitative finance and emerging domains at the intersection of finance, technology and sustainability.

Activities

QFinLab supports research dissemination and community building through seminars, workshops, conferences, training initiatives and outreach activities. These initiatives connect researchers, students, institutions and financial-industry practitioners.

• QFinLab Seminar. A scientific seminar series devoted to current research topics in quantitative finance, fintech, risk management and related areas.
• Climate Risk Seminar. A thematic seminar series on climate risk and its implications for economic activity, financial assets, insurance products, risk management, transition policies and incentive mechanisms.
• ALGODEFI Conference. A conference on Algorithmic Trading, Decentralized Finance and Artificial Intelligence in Capital Markets, designed to foster discussion between academia and industry on trading, market microstructure, machine learning, digital assets, smart contracts and cryptocurrencies.
• International Fintech Research Conference. QFinLab researchers are among the promoters of this annual research initiative. The first edition took place at Politecnico di Milano on October 27-28, 2022, and aimed to bring together researchers working across fintech, including banking, asset management, insurance, payments, capital markets, digital currencies, cybersecurity, blockchain and artificial intelligence.
• Green Finance Workshop. QFinLab and the Department of Mathematics promote this initiative on quantitative methods for green finance. The first workshop was held in Milan on February 1-2, 2024, at Politecnico di Milano.
• FinRiskAlert and outreach initiatives. QFinLab also contributes to dissemination and financial education through initiatives aimed at institutions, schools, practitioners and the wider public. Click here for details.

Main Recent Financed Projects

• Securing Decentralized Finance and Remote Healthcare Systems – SHIELD (2024-2025). PNRR project PE7 Security and rights in the cyberspace (SERICS). The SHIELD project aims to develop protective measures to safeguard users of DeFi systems from cyber fraud, as well as tools to prevent, detect and respond to cyber threats targeting medical devices and patients’ personal information.
• Measuring, managing and hedging indirect climate-transition risk (2023-2025). 2022 PNRR PRIN. The project studies transition risk for firms and societies, focusing mainly on indirect risks arising from supply chains and from the interconnections among economic activities.
• Caccia al Tesoro Finanziaria – Financial Education Gamification (2023). The project supported the creation of an online event for secondary school students on financial education, exploiting gamification.
• MUSA – Multilayered Urban Sustainability Action (2022-2025). PNRR Project.  MUSA aims to transform the metropolitan area of Milan into an ecosystem of innovation for urban regeneration, intervening in fields ranging from social innovation to technology, with the goal of becoming a national and European model.
• EMFI – Emergency Finance (2021-2022). Research project proposed by Politecnico di Milano, Cefriel and University of Stirling, financed by the Algorand Foundation. The project focused on research and development of a special purpose digital currency on the Algorand blockchain.
• FIN-TECH – A FINancial supervision and TECHnology compliance training programme (2019-2021). H2020 project. The project provided training for Consob staff on fintech topics, including big data, machine learning and blockchain.
• IMPACT EDUFIN (2020-2022). Bank of Italy “Contributo liberale”. The project supported financial education activities in Italian secondary schools and an impact analysis aimed at assessing their effectiveness.
• EDUFIN@POLIMI (2018-2020). Fondazione Cariplo project on financial education in schools. The project funded the creation of financial education material and its dissemination in secondary schools in Milan.

Research Output

• Apr 23 2026 Pricing and Hedging Financial Derivatives in Merger & Acquisition Deals with Price Impact Pricing and Hedging Financial Derivatives in Merger & Acquisition Deals with Price Impact by Emilio Barucci, Yuheng Lan, Daniele Marazzina. Preprint Abstract: We investigate the optimal execution of contracts that are used in merger\&acquisition deals. We consider cash-settled and physically delivered contracts between a broker and a counterpart. Contracts are linear (total returns swaps), nonlinear (collar contracts) or Asian type (TWAP based contracts). We derive the optimal execution strategy and the optimal fee through indifference utility arguments allowing for linear market effects of trades. We show that linear cash-settled contracts are more expensive and more exposed to manipulation/statistical arbitrages by the broker. Also nonlinear and Asian type contracts are exposed to these phenomena.

• Apr 17 2026 Navigating Supply Shocks: Sector Resilience and Production Prices Through Stochastic Input–Output Modeling Navigating Supply Shocks: Sector Resilience and Production Prices Through Stochastic Input–Output Modeling by Giovanni Amici, Gianluca Fusai, Anna Maria Gambaro, Daniele Marazzina. Published in Mathematical Finance. Abstract: This study develops a novel multivariate stochastic framework for assessing systemic risks, such as climate and nature-related shocks, within production or financial networks. By embedding a linear stochastic fluid network, interpretable as a generalized vector Ornstein–Uhlenbeck process, into the production network of interdependent industries, the model captures how physical shocks (e.g., extreme climate events or geopolitical disruptions) propagate through input–output (IO) linkages and affect sectoral price dynamics. The framework extends traditional IO models with advanced stochastic and dynamic features, enabling a quantification of both direct and indirect transmission channels of supply-cost shocks to production prices. Contributing to the literature on stochastic IO and Markovian networks, the model introduces the concept of divisible shocks, allowing for finer-grained simulation of adaptation responses and resilience across sectors. Empirical calibration leverages real-world economic data, including IO tables and historical industrial price indices. Sensitivity analyses are conducted using distributional risk measures, offering new tools for climate stress testing and medium to long-term risk assessment. Our findings support the optimal design of supply risk management strategies, including policy interventions and decentralized adaptation incentives for systemic stability under environmental stress.

• Apr 17 2026 Forecasting Bitcoin price movements using multivariate Hawkes processes and limit order book data Forecasting Bitcoin price movements using multivariate Hawkes processes and limit order book data by Davide Raffaelli, Raffaele Giuseppe Cestari, Daniele Marazzina, Simone Formentin. Published in Decisions in Economics and Finance. Abstract: Forecasting short-term returns of Bitcoin is a key challenge in high-frequency trading, due to the cryptocurrency’s extreme volatility, market microstructure complexity, and non-stationary behavior. Limit Order Book (LOB) data offer a rich source of high-resolution information that can improve predictive models beyond what is possible using price series alone. In this study, we investigate the BTC/USD trading pair and develop two return sign forecasting approaches based on multivariate Hawkes processes (MHP), leveraging LOB event streams collected in real time from a centralized exchange. Our first method integrates an MHP with a Continuous-time Output Error (COE) model to jointly model event timing and return dynamics in irregularly sampled data. The second approach directly forecasts return sign and timing via an extended MHP. Empirical results show that the hybrid MHP–COE pipeline consistently outperforms the pure Hawkes-based model in both prediction accuracy and simulated trading profitability. We also evaluate the methods’ computational efficiency to assess their viability in real-world high-frequency environments.

• Jan 29 2026 A Standardized Approach to Environmental, Social, and Governance Ratings for Business Strategy: Enhancing Corporate Sustainability Assessment A Standardized Approach to Environmental, Social, and Governance Ratings for Business Strategy: Enhancing Corporate Sustainability Assessment by Francesca Grassetti and Daniele Marazzina. Published in Sustainability. Abstract: The current landscape of Environmental, Social, and Governance (ESG) ratings is fragmented by methodological inconsistencies, lack of standardization, and substantial divergences among rating providers. These discrepancies hinder comparability, reduce transparency, and undermine the reliability of ESG assessments, limiting their effectiveness for both investors and corporate decision-makers. To address these issues, this study introduces a standardized approach to ESG rating construction, aimed at enhancing the objectivity and interpretability of corporate sustainability evaluations. The methodology integrates the Global Reporting Initiative standards with the United Nations Sustainable Development Goals, thereby identifying a coherent set of key performance indicators across the ESG pillars. By relying solely on publicly available data and incorporating mechanisms for managing missing information, the model provides a transparent and reproducible framework for sustainability assessment. Its validity is demonstrated through an empirical application to firms in the financial and manufacturing sectors across Europe and the United States, with benchmarking against established ratings from providers. Rather than replicating existing ESG scores, the model offers a transparent and reproducible alternative built on disclosed performance data, without relying on forward-looking statements, corporate promises, or commercial data providers. By penalizing non-disclosure and enabling sector-specific sensitivity analysis, the framework supports more accountable and customizable sustainability assessments, helping align ESG evaluations with strategic and regulatory priorities.