Teaching
I have taught at the BSc/BAA, MSc, and PhD levels — finance, financial engineering, and quantitative methods — at Université Laval, the University of Neuchâtel, and HEC Montréal, and as a visiting professor at Paris Dauphine–PSL, HEC Liège, and EHL Hospitality Business School in Lausanne.
My courses are deliberately applied. Coming from industry, I teach with real data and real constraints, and I want students to leave with methods they can actually use — I like bringing a quant mindset into a business-school classroom. I currently teach statistical and machine-learning methods for financial data in the MSc in Financial Engineering at HEC Montréal, in both French and English sections. Teaching matters to me, and it seems to land: I have received teaching awards at each of the three universities where I have held a faculty position.
Students
PhD students: I am always looking for strong candidates in finance, financial engineering, and data science to join the team (see the PhD in Financial Engineering). MSc students: I supervise MSc theses and supervised projects only for students enrolled at HEC Montréal (including projects with industry partners); if that's you and you'd like to work with me, get in touch with a short note on your interests and timeline.
Postdoctoral researchers
- Robust Estimation and Inference with High-Dimensional Time Series, Rosnel Sessinou, HEC Montréal, 2022–2024
- Media Bias and the Informational Efficiency of Capital Markets, Keven Bluteau, SNSF 191730, 2020–2021
PhD students
- Roosevelt Nguemo (HEC Montréal, 2026–, co-supervised with Tolga Cenesizoglu)
- Murilo Pereira (HEC Montréal, 2024–, co-supervised with Debbie Dupuis)
- Janos Füting (HEC Montréal, 2023–, co-supervised with Debbie Dupuis)
- Shayan Nazemi (HEC Montréal, 2023–, co-supervised with Tolga Cenesizoglu)
- Céleste Hardy (HEC Liège, 2022–, co-supervised with Marie Lambert)
- Olivier Delmarcelle (Ghent University, 2020–, co-supervised with Kris Boudt)
- The Importance of Empirical Choices: Three Essays on Retail Trading and Hedging Strategies, Clément Aymard (HEC Montréal, 2020–2026, co-supervised with Tolga Cenesizoglu)
- Three Essays on Price Formation and Liquidity in Financial Markets, Emanuele Guidotti (University of Neuchâtel, 2019–2023, co-supervised with Tim Kroencke)
- Sentiment and Econometrics: Toward a Unified Framework of Textual Sentiment Analysis for Economic and Financial Applications, Samuel Borms (University of Neuchâtel, 2017–2020, co-supervised with Kris Boudt)
- Modeling Latent Variables in Economics and Finance, Keven Bluteau (University of Neuchâtel, 2016–2019, co-supervised with Kris Boudt)
MSc theses
- Does Unexpected Change in Climate Risk Concerns Affects IV Surfaces Dynamic? A VAR Approach Using UMC Index, Shihao Tong (September 2025)
- Strategies to Monetize the Sentiment Extracted With NLP Techniques From Earnings Call Transcripts, Benjamin Séguin (September 2025)
- Can Top ESG Stocks Outperform the Investment Universe? Evidence From Mean-Variance Efficiency and Spanning Tests, Fatma Ammar (March 2025)
- Examining the Relation Between Stocks' Option-Implied Moments and Climate Change Concerns, Thomas Boyer (October 2024)
- Realized Variance Forecasting in the US Stock Market, Frédéric Rivard (September 2024)
- Impact of Climate Change Concerns on the Volatility of Green and Brown Stocks, Kriti Bhaya (September 2024)
- Weathering the Markets: Exploring the Empirical Relationship Between Climate Change Concerns and Commodity Futures Contracts, Michael Pimentel (March 2024)
- Regime-Switching Correlations with Exogenous Economic Variables, Paul Kelendji (March 2024, co-directed with Geneviève Gauthier)
- Comparaison de modèles factoriels linéaires et non-linéaires pour l'univers des actions du S&P 500, Frédéric Siino (October 2023)
- The Peer Performance Ratios in Cryptocurrency Markets, Abhishek Duggal (August 2023)
- Multi-Asset Approach to Realized Variance Forecasting: Empirical Evidence from the Canadian Dollar, Alexandre Turenne (August 2022)
- Application of Textual Sentiment Scores in Value-at-Risk models, Jaime Casigay (March 2022)
Supervised projects
Beyond theses, my 70+ MSc supervised projects (many with industry partners) span:
- Volatility and risk forecasting — GARCH / MSGARCH / GAS models, realized-volatility prediction, Value-at-Risk, and model combinations across equities, FX, commodities, and crypto.
- Climate finance and ESG — climate-concern indices and the performance of green versus brown stocks, ESG investing, and sustainability-reporting analysis.
- Textual sentiment and NLP — sentiment and economic-policy-uncertainty indices from news and social media, topic modeling, and text-based prediction (inflation, gold, the VIX).
- Credit and default risk — probability-of-default modeling, including machine-learning approaches such as random forests on bank-loan portfolios.
- Portfolio construction and asset allocation — risk-based and factor portfolios, sector rotation, copula models, and private-debt and real-estate allocation.
- Quantitative finance and derivatives — interest-rate and swap modeling, option Greeks, liquidity risk, and systematic trading strategies.