Lars Stentoft

Associate Professor
Canada Research Chair in Financial Econometrics
Joint with the Department of Economics
Office: WSC 278
Phone: 519-661-2111 x85311


Research Areas

  • Computational Finance
  • Finance
  • Financial Econometrics
  • Option Pricing
  • Simulation Methods

Graduate Students Supervision

  • Francois Michel Boire


  • Stentoft, L. (2015). What we can learn from pricing 139,879 individual stock options. Journal of Derivatives, 22 (4): 54-78.
  • Rombouts, J. V. K. and Stentoft, L. (2015). Option pricing with asymmetric heteroskedastic normal mixture models. International Journal of Forecasting, 31 (3): 635-650.
  • Boyer, M. M., Stentoft, L. and Dorion, C. (2015). Les Modèles factoriels et la gestion du risque de longévité. Actualité Économique, 91(4) 36 .
  • Rombouts, J. V. K., Stentoft, L. and Violante, F. (2014). The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options. International Journal of Forecasting, 30 (1): 78-98.
  • Rombouts, J. V. K. and Stentoft, L. (2014). Bayesian option pricing using mixed normal heteroskedasticity models. Computational Statistics and Data Analysis, 76 588-605.
  • Boyer, M. M., Mejza, J. and Stentoft, L. (2014). Measuring longevity risk: An application to the royal Canadian mounted police pension plan. Risk Management and Insurance Review, 17 (1): 37-59.
  • Létourneau, P. and Stentoft, L. (2014). Refining the least squares Monte Carlo method by imposing structure. Quantitative Finance, 14 (3): 495-507.
  • Stentoft, L. (2014). Value function approximation or stopping time approximation: A comparison of two recent numerical methods for American option pricing using simulation and regression. Journal of Computational Finance, 18 (1): 65-120.
  • Stentoft, L. and Boyer, M. M. (2013). If we can simulate it, we can insure it: An application to longevity risk management. Insurance: Mathematics and Economics, 52 (1): 35-45.
  • Stentoft, L. (2013). American option pricing using simulation with an application to the GARCH model. Handbook of Research Methods and Applications in Empirical Finance, 114-147.
  • Denault, M., Simonato, J. G. . and Stentoft, L. (2013). A simulation-and-regression approach for stochastic dynamic programs with endogenous state variables. Computers and Operations Research, 40 (11): 2760-2769.
  • Stentoft, L. (2012). American Option Pricing Using Simulation and Regression: Numerical Convergence Results. Springer Proceedings in Mathematics and Statistics, 19: 57-94.
  • Boyer, M. M., Favaro, A. and Stentoft, L. (2012). Pricing Survivor Forwards and Swaps in Incomplete Markets Using Simulation Techniques. Longevity Risk Management for Institutional Investors, Fall 69-87.
  • Stentoft, L. and Rombouts, J. V. K. (2011). Multivariate option pricing with time varying volatility and correlations. Journal of Banking and Finance, 35 (9): 2267-2281.
  • Stentoft, L. (2011). American option pricing with discrete and continuous time models: An empirical comparison. Journal of Empirical Finance, 18 (5): 880-902.