Lars Stentoft
Lars Stentoft is an Associate Professor at Department of Economics (joint with Department of Statistical and Actuarial Sciences), University of Western Ontario, Canada. His research focuses on computational finance, where he is concerned with developing numerical methods for solving dynamic programming problems in general and valuing options in particular. He has also been working on longevity risk and on the pricing of longevity risk derivatives. His research has been published in, e.g., Journal of Banking and Finance, Journal of Empirical Finance, Journal of Financial Econometrics, Management Science, and Quantitative Finance.
Areas of Interest
Finance; Financial Econometrics; Computational Finance; Econometrics.
Publications
Letourneau, P. and L. Stentoft. (2022), ‘Simulated Greeks for American Options’, forthcoming in Quantitative Finance.
Huddleston, D. Liu, F. and L. Stentoft. (2021), ‘Intraday Market Predictability: A Machine Learning Approach’, Journal of Financial Econometrics, nbab007, (https://doi.org/10.1093/jjfinec/nbab007).
Francois, P. and L. Stentoft. (2021), ‘Smile-Implied Hedging with Volatility Risk’, Journal of Futures Markets, 41(8), 1220-1240, (https://doi.org/10.1002/fut.22191).
Liu, F. and L. Stentoft. (2021), ‘Regulatory Capital and Incentives for Risk Model Choice under Basel 3’, Journal of Financial Econometrics, 19(1), 53-96, (https://doi.org/10.1093/jjfinec/nbaa029).
Escobar, M., Rastegeri, J. and L. Stentoft. (2021), ‘Option Pricing with Conditional GARCH Models’, European Journal of Operational Research, 289(1), 350-363, (https://doi.org/10.1016/j.ejor.2020.07.002).
Rombouts, J., L. Stentoft and F. Violante. (2020), ‘Dynamics of Variance Risk Premia: A New Model for Disentangling the Price of Risk’, Journal of Econometrics, 217(2), 312-334, (https://doi.org/10.1016/j.jeconom.2019.12.006).
Rombouts, J., L. Stentoft and F. Violante. (2020), ‘Pricing Individual Stock Options using both Stock and Market Index Information’, Journal of Banking and Finance, 111, #105727, 1-16, (https://doi.org/10.1016/j.jbankfin.2019.105727).
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).
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).
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).
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).
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. (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).