Louis Bélisle(Pronounced "BAY-lil")
PhD candidate, currently on the 2020-2021 Job Market
Department of Economics, University of Toronto
150 St. George St., GE350
Toronto, ON, M5S 3G7, Canada
Research Specialization: Financial Econometrics, Industrial Organization of Banking
I am a PhD candidate at the University of Toronto and I am currently on the job market. I develop econometric tools to analyse the industrial organization of banking with respect to systemic risk and financial stability. I am in my fifth year of the PhD and my current working papers explore various aspects of regulatory impacts on banking and lending.
In my Job Market Paper, I am currently researching how the participation of non-banks in the lending market may increase the systemic risk in the economy. Using a structural model of the syndicated loans market, I show that blanket capital requirements on all lenders, while encouraging banks to derease risk exposure, may encourage non-banks lenders to take greater risks. This effect is driven by the increase in the cost of capital due to the regulation, and the different risk-aversion preferences of non-banks when compared to banks. I will be presenting a poster version of this paper at the 2021 AFA in January. I would have presented at the CEA and at the SCSE in May 2020, but the world went on lockdown, so if you are interested, please get in contact with me. I'll update my website about future presentations.
In my other paper, titled Time-Varying Elasticity of Substitution in Near-Money Assets (presented at the 29th Annual Meeting of the Midwest Econometrics Group), I show that the Liquidity Coverage Ratio (LCR) mandated by Basel III might be misguided due to some varying elasticity of substitution between money and safe assets used to cover that liquidity. Understanding how some assets are substitutes for money gives us a better sense of how sensitive the economy might be to potential liquidity shocks.
I also have a publication in collaboration with Martin Burda. In this paper, we develop and analyse the performance of a Constrained Hamiltonian Monte Carlo method to estimate a Copula Multivariate GARCH process. We find benefits for efficient inference on complicated constrained dependence structures when compared to a traditional random-walk methodology.