Sigrist Fabio HSLU W

Prof. Dr. Fabio Sigrist Fabio Sigrist

Lecturer

Sigrist Fabio HSLU W

Lucerne School of Business

Room 6.37
Campus Zug-Rotkreuz
Suurstoffi 1
6343 Rotkreuz


Institute of Financial Services Zug IFZ

CC Financial Services

Lecturer and Project Manager

+41 41 757 67 61

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Biography

Education

  • 2009 - 2013 PhD in Statistics, ETH Zurich
  • 2003 - 2008 MSc in Mathematics (with distinction), ETH Zurich
                              (+study abroad at Sapienza University of Rome)
  • 2006 - 2008 MEd in Mathematics education, ETH Zurich

Professional experience

  • 2018 - today:  Professor of Applied Statistics and Data Science, Lucerne University of Applied Sciences and Arts, Institute of Financial Services Zug IFZ
  • 2016 - today: Lecturer in Statistics (Stochastic Simulation / Monte Carlo Methods, Applied Multivariate Statistics, Bayesian Statistics), ETH Zurich
  • 2015 - 2018: Lecturer, Lucerne University of Applied Sciences and Arts, Institute of Financial Services Zug IFZ
  • 2013 - 2015: Risk Management Consultant, EVMTech AG
  • 2009 - 2013: Assistant, Seminar for Statistics, ETH Zurich
  • 2011: Research Stay at University of Washington, Seattle
  • 2009 - 2011: Statistical Consultant for the European Credit & Surety PML Study
  • 2008 and 2013: Internships at SCOR and Swiss Re in Risk Management and Credit Teams

Publications

Software

  • GPBoost (C++ library for combined tree-boosting and random effects / Gaussian process models)
  • KTBoost (Python package for tree-boosting)
  • spate (R package for spatio-temporal modeling)
Teaching and research focus
  • Machine Learning, Spatial Statistics, Financial Econometrics, Credit Risk, Analytics

Projects

Research projects

  • IFZ FinTech
  • NISMO: Novel Interpretable Spatial Machine Learning for Hedonic Real Estate Modeling
  • Asset Allocation through Reinforcement Learning for Swiss Pension Funds
  • CreHos: Novel Machine Learning Algorithms and Alternative Data Sources for Better Credit Risk Assessment for the Hospitality Sector
  • Data Driven Banking
  • Finanzmanagement für Gemeinden
  • The Use of Machine Learning and Transaction Data for Credit Risk Assessment
  • Space-time machine learning models for valuation and prediction of real estate objects
  • SentiVol: Sentiment Analysis and Bayesian Model Averaging for Volatility Prediction
  • SentiVol: Sentiment Analysis and Bayesian Model Averaging for Volatility Prediction
  • Recomender System for Data-Driven Leads for Cross Selling in Banking
  • Machine learning based fraud detection and credit risk assessment for SME credits
  • Prediction of third-party wealth for bank customers
  • Studie zur Finanzierung der KMU in der Schweiz 2016
  • Development of a recommender system for a retail bank
  • Regulierungsfolgen bei Vermögensverwaltern
  • SNF Liquidität Basel lll

Publications and presentations

Publications and presentations - Book/catalogue

  • Ankenbrand, Thomas; Bieri, Denis; Braunwalder, Fabian; Brunner, Ursin; Dietrich, Andreas; Kaelin, Gregor; Kronenberger, Timon; Ott, Daniel; Reda Kouba, Ranja; Sigrist, Fabio; Viejra, Andrew & Walser, Tanja (2023). Data-driven Banking in Switzerland. Data drives the next generation of banking. Zug: Verlag IFZ - Hochschule Luzern.

Publications and presentations - Article, review; peer reviewed

  • Leuenberger, Nicola & Sigrist, Fabio (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research, 2023(3), 1390-1406.
  • Sigrist, Fabio (2023). Latent Gaussian Model Boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023, 1894-1905.
  • Sigrist, Fabio (2022). Gaussian Process Boosting. Journal of Machine Learning Research (JMLR), 2022(23), 1-46.
  • Audrino, Francesco; Ballinari, Daniele & Sigrist, Fabio (2022). When does attention matter? The effect of investor attention on stock market volatility around news releases. International Review of Financial Analysis, 2022(82), 102185.
  • Dambon, Jakob; Fahrländer, Stefan; Karlen, Saira; Lehner, Manuel; Schlesinger, Jaron; Sigrist, Fabio & Zimmermann, Anna (2022). Examining the Vintage Effect in Hedonic Pricing using Spatially Varying Coefficients Models: A Case Study of Single-Family Houses in the Canton of Zurich. Swiss journal of economics and statistics / ed. by the Swiss Society of Economics and Statistics / hrsg. von der Schweiz. Gesellschaft für Volkswirtschaft und Statistik / publ. par la Société suisse d'économie et de statistique, 1.
  • Sigrist, Fabio (2021). KTBoost: Combined kernel and tree boosting. Neural Processing Letters, 2021(2), 1147-1160.
  • Sigrist, Fabio (2021). Gradient and newton boosting for classification and regression. Expert Systems With Applications, 2021(167), 114080.
  • Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2021). Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction. Spatial Statistics, 1. doi: 10.1016/j.spasta.2020.100470
  • Audrino, Francesco; Sigrist, Fabio & Ballinari, Daniele (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting, 2020(2), 334-357.
  • Sigrist, Fabio; Lengwiler, Christoph & Köchli, Patrick (2018). Determinants of municipal loan spreads: empirical evidence from Switzerland. Financial Markets and Portfolio Management (formerly: Finanzmarkt und Portfolio Management), 2018(Volume 33, Februar bis November 2018), 1-24.
  • Sigrist, Fabio (2015). spate: an R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process. Journal of Statistical Software, 1-13.
  • Sigrist, Fabio (2015). Stochastic partial differential equation based modelling of large space–time data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 3-33.
  • Sigrist, Fabio (2012). A dynamic nonstationary spatio-temporal model for short term prediction of precipitation. The Annals of Applied Statistics, 1452-1477.
  • Sigrist, Fabio (2011). An autoregressive spatio-temporal precipitation model. Procedia Environmental Sciences, 2-7.
  • Sigrist, Fabio (2011). Using the censored gamma distribution for modeling fractional response variables with an application to Loss Given Default. Astin Bulletin, 673-710.

Publications and presentations - Article, review; not peer reviewed

  • Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2021). Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models. arXiv, 1.
  • Hirnschall, Christoph & Sigrist, Fabio (2019). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance, 2019(102), 177-192.

Publications and presentations - Chapter/legal commentary/lexicon article

  • Dietrich, Andreas; Mattmann, Brian; Sigrist, Fabio & Wanzenried, Gabrielle (2018). Liquiditätsvorschriften. In Armin Jans; Christoph Lengwiler; Marco Passardi (Hrsg.), Krisenfeste Schweizer Banken? (S. 429-455). Zürich: NZZ Libro.

Publications and presentations - Other publication formats

  • Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (11.10.2019). varycoef: Modeling Spatially Varying Coefficients [Softwareprogramm]. https://cran.r-project.org/web/packages/varycoef/index.html