Welcome at LFIN !
LFIN, Louvain Finance, is a research center of the Université catholique de Louvain, located in Louvain-la-Neuve (Belgium). It has been founded in 2016 and it now houses about 11 professors, and 15 researchers.
LFIN promotes and contributes to academic research in Finance via its research projects and the organization of top-level PhD courses, conferences and other public research activities. The Center’s main area of expertise are related to most of the areas of finance, including Asset Pricing, International Finance, Asset and Risk Management, Mathematical Finance, Macro-Finance, Market Microstructure, Corporate Finance and Behavioral Finance.
The center is leading several innovative projects, such as the first Bloomberg learning center in Belgium and it is supported in its activities by leading private institutions such as TreeTop Asset Management and Candriam.
Together with CORE, IRES, ISBA, LFIN, and SMCS, LFIN is part of the Institute of Multidisciplinary Research for Quantitative Modeling and Analysis (LIDAM) where researchers develop and use a coherent set of tools and methods for quantitative modeling and analysis in their various fields of expertise.
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7 October 2026Antoine Trotin receives a Special Mention at the 2026 Camille Gutt PrizeAntoine Trotin, Research & Teaching Assistant at the Louvain School of Management and researcher at UCLouvain-LIDAM/LFIN, has received a Special Mention at the 2026 Camille Gutt Prize for his Master’s thesis on the influence of weather conditions on financial markets in Belgium. -
19 November 2025Catherine D’Hondt joins the Eligibility Committee of the Towards Sustainability LabelLIDAM and the Louvain School of Management are pleased to announce that Catherine D’Hondt, Professor of Finance and researcher within LFIN, has been appointed to the Eligibility Committee of the Towards Sustainability Labeling Agency (CLA).
Upcoming LFIN Events
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LIDAM Finance Seminar - Massimiliano Marcellino
30/10/2026 - 11:00 - LIDAM D.251
Massimiliano Marcellino
(Bocconi University)
will give a presentation on
Matrix Three-Pass Regression Filter: A Nowcasting Framework for Mixed-Frequency Multidimensional Datats
Abstract:
We contribute to the literature on large multidimensional data by proposing a Matrix Mixed-Frequency Three-Pass Regression Filter (Matrix MF–TPRF) for nowcasting with high-dimensional, ragged-edge information sets. The method extends proxy-based factor extraction to matrix-valued predictors, preserving cross-sectional structure while accommodating mixed frequencies and missing observations. After discussing the procedure and its properties, Monte Carlo evidence shows that exploiting the matrix structure improves predictive-signal recovery and nowcasting performance relative to vector approaches, especially with persistent non-predictive components. A pseudo real-time application to GDP growth in eight major euro-area countries indicates sizeable gains at early vintages and in the post-Covid period, when cross-country co-movement is stronger. The results also highlight the importance of parsimonious information sets for stable real-time performance.
More information about the speaker
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