Geneviève Gauthier, SSC Award for Impact of Applied and Collaborative Work 2018

Geneviève Gauthier
SSC Award for Impact of Applied and Collaborative Work

The 2018 recipient of the Statistical Society of Canada Award for Impact of Applied and Collaborative Work is Geneviève Gauthier, Professor of Statistics in the Department of Decision Sciences at HEC Montréal. The award recognizes outstanding contributions by a member of the SSC in collaborative research and applied work, the importance of which derives primarily from its relatively recent impact on a subject area outside of the statistical sciences, on an area of application, or on an organization.

Born in Montréal, Geneviève Gauthier studied at Université du Québec à Montréal (BSc 1989, MSc 1991) and Carleton University (PhD 1996). Her Master’s thesis on autoregressive models for integer-valued time series was written under the guidance of Alain Latour and her doctoral dissertation on stochastic differential equations was supervised by Don Dawson. She joined HEC Montréal in 1996, where she gradually rose through the ranks. She was promoted to full professorship in 2008, visited the University of Auckland during a sabbatical leave in 2012-13, and was Department Chair in 2013-16.

Geneviève’s current research is concerned with the use of stochastic filtering techniques to fit models for dynamic latent risk factors using financial products traded on the markets. With her collaborators in finance and insurance, she developed modeling strategies for serial and cross-sectional dependence between high-frequency time series that led to a better understanding of the risk of contagion and its potentially catastrophic consequences. The filtering methods that they introduced make it possible to fit much more complex and realistic market models than ever before. For example, these methods can be used to assess firm-specific default probabilities and other risk factors such as loss given default. In contrast, the standard approach to credit risk relies on credit rating agencies, which provide only aggregate information per credit rating.

Another major challenge picked up by Geneviève stems from the fact that the pricing of financial contracts is related to an expectation with respect to a change of measure that contains parameters of its own. Traditionally these parameters were selected by minimizing the error between the theoretical and market prices. In collaborative work, Geneviève showed that a much more efficient way to select an equivalent martingale measure is to exploit high frequency option prices coupled with filtering techniques. In recent years, she also developed a sophisticated multivariate prediction model for the electricity market, whose prices yield highly non-stationary time series which exhibit both seasonality and daily spikes. The model allows electricity retailers to deploy efficient short-term hedging strategies.

Beyond her scholarly work, Geneviève has had a significant impact through training and consulting. At HEC Montréal she played a major role in shaping the financial engineering program and supervised to completion 70 graduate students, including seven doctoral students. She has also done extensive consulting for companies from the public and private sectors, e.g., helping banks to implement high-level stochastic models for the computation of capital reserves. In addition, she frequently serves as an external auditor to validate credit risk models for various financial institutions.

The excellence of Geneviève’s collaborative work was recognized with the Best Paper on Derivatives Award at the 2017 Northern Finance Association conference held in Halifax and the Best Paper Award in the Accounting and Finance Section at the 2012 World Business and Economics Research Conference held in Auckland. In her private life Geneviève is a skier and an outdoor enthusiast. Her two teenagers and the family farm also keep her very busy at home.

The citation for the award reads: 

“To Geneviève Gauthier, for her outstanding contributions to the promotion of innovative statistical methodologies in financial engineering, and in the training of highly qualified personnel.”


Thanks to Christian Genest, who was primarily responsible for producing this material.