Lars Stentoft

Associate Professor
Joint with the Department of Economics
Office: WSC 278
Phone: 519-661-2111 x85311
Email: lars.stentoft@uwo.ca


Personal Website

 

Research Areas

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

Graduate Students

  • Francois Michel Boire (PhD)
  • Andrew Boyko (PhD)
  • Xinxin Li (MSc)
  • Junjun Liu (MSc)
  • Sahab Zandi (MSc)

Publications

  • Stentoft, L. and S. Wang. (2019), ‘Consistent and Efficient Dynamic Portfolio Replication with Many Factors’, forthcoming in Journal of Portfolio Management.
  • Rombouts, J., L. Stentoft and F. Violante. (2019), ‘Variance Swap Payoffs, Risk Premia and Extreme Market Conditions’, forthcoming in Econometrics and Statistics, (https://doi.org/10.1016/j.ecosta.2019.05.003).
  • Rombouts, J., L. Stentoft and F. Violante. (2019), ‘Dynamics of Variance Risk Premia: A New Model for Disentangling the Price of Risk’, forthcoming in Journal of Econometrics.
  • Stentoft, L. (2019), ‘Efficient Numerical Pricing of American Call Options using Symmetry Arguments’, Journal of Risk and Financial Management, 12 #59, 1-26 (https://doi.org/10.3390/jrfm12020059).
  • Grynkiv, G. and L. Stentoft. (2018), ‘Stationary Threshold Vector Autoregressive Models’, Journal of Risk and Financial Management, 11 #45, 1-23 (https://doi.org/10.3390/jrfm11030045).
  • Boyer, M.M. and L. Stentoft. (2017), ‘Yes We Can (Price Derivatives on Survivor Indices)’, Risk Management and Insurance Review, 20(1), 37-62 (https://doi.org/10.1111/rmir.12073).
  • Boyer, M.M., C. Dorion and L. Stentoft. (2015), ‘Les Modèles factoriels et la gestion du risque de longévité’, L’Actualité Économique, 91(4) #6 (https://doi.org/10.7202/1037212ar).
  • Rombouts, J. and L. Stentoft (2015), ‘Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models’, International Journal of Forecasting, 31(3), 635-650 (https://doi.org/10.1016/j.ijforecast.2014.09.002).
  • Stentoft, L. (2015), ‘What We Can Learn From Pricing 139,879 Individual Stock Options’, Journal of Derivatives, 22(4), 54-78 (https://doi.org/10.3905/jod.2015.22.4.054).
  • 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), 1-56 (https://doi.org/10.21314/JCF.2014.281).
  • Rombouts, J. and L. Stentoft (2014), ‘Bayesian Option Pricing using Mixed Normal Heteroskedasticity Models’, Computational Statistics & Data Analysis, 76, 588-605 (https://doi.org/10.1016/j.csda.2013.06.023).
  • Boyer, M.M., J. Mejza and L. Stentoft. (2014), ‘Measuring Longevity Risk: An Application to the Royal Canadian Mounted Police Pension Plan’, Risk Management & Insurance Review, 17(1), 37-59 (https://doi.org/10.1111/rmir.12018).
  • Létourneau, P. and L. Stentoft (2014), ‘Refining the Least Squares Monte Carlo Method by Imposing Structure’, Quantitative Finance, 14(3), 495-507 (https://doi.org/10.1080/14697688.2013.787543).
  • Rombouts, J., L. Stentoft and F. Violante. (2014), ‘The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options’, International Journal of Forecasting, 30, 78-98 (https://doi.org/10.1016/j.ijforecast.2013.07.006).
  • Denault, M., J.-G. Simonato & L. Stentoft (2013), ‘A Simulation-and-Regression Approach for Stochastic Dynamic Programs with Endogenous State Variable’, Computers & Operations Research 40 (11), 2760-2769 (https://doi.org/10.1016/j.cor.2013.04.008).
  • Boyer, M.M. and L. Stentoft. (2013), ‘If we can simulate it, we can insure it: An application to longevity risk management’, Insurance: Mathematics and Economics 52 (1), 35-45 (https://doi.org/10.1016/j.insmatheco.2012.10.003).
  • Boyer, M.M., A. Favaro and L. Stentoft. (2012), ‘Pricing Survivor Forwards and Swaps in Incomplete Markets Using Simulation Techniques’, Longevity Risk Management for Institutional Investors, Fall 2012, 69-87.
  • Stentoft, L. (2011), ‘American Option Pricing with Discrete and Continuous Time Models: An Empirical Comparison’, Journal of Empirical Finance 18 (5), 880-902 (https://doi.org/10.1016/j.jempfin.2011.09.004).
  • Rombouts, J. and L. Stentoft. (2011), ‘Multivariate Option Pricing with Time Varying Volatility and Correlations’, Journal of Banking and Finance 35, 2267–2281 (https://doi.org/10.1016/j.jbankfin.2011.01.025).
  • Stentoft, L. (2008), ‘American Option Pricing Using GARCH models and the Normal Inverse Gaussian Distribution’, Journal of Financial Econometrics 6 (4), 540-582 (https://doi.org/10.1093/jjfinec/nbn013).
  • Stentoft, L. (2005), ‘Pricing American Options when the Underlying Asset Follows GARCH Processes’, Journal of Empirical Finance 12 (4), 576-611 (https://doi.org/10.1016/j.jempfin.2004.08.001).
  • Stentoft, L. (2004), ‘Convergence of the Least Squares Monte Carlo Approach to American Option Valuation’, Management Science 50 (9), 1193-1203 (https://doi.org/10.1287/mnsc.1030.0155).
  • Stentoft, L. (2004), ‘Assessing the Least Squares Monte-Carlo Approach to American Option Valuation’, Review of Derivatives Research 7 (3), 129-168 (https://doi.org/10.1023/B:REDR.0000031176.24759.e6).
  • Brendstrup, B., S. Hylleberg, M. Nielsen, L. Skipper and L. Stentoft. (2004), ‘Seasonality in Economic Models’, Macroeconomic Dynamics 8 (3), 362-394 (https://doi.org/10.1017/S1365100504030111).

Book Chapters

  • Stentoft, L. (2013), ‘American Option Pricing using Simulation with Application to the GARCH Model’, in Handbook of Research Methods and Applications in Empirical Finance, Edited by Adrian R. Bell, Chris Brooks and Marcel Prokopczuk, Chapter 5, 114-147.
  • Stentoft, L. (2012), ‘American Option Pricing using Simulation and Regression: Numerical Convergence Results’, in Topics in Numerical Methods for Finance, Springer Proceedings in Mathematics & Statistics 19, Edited by M. Cummins, F. Murphy and J.J.H. Miller, 57-94.