Fetching the paper…
Reading the bibliography…
We propose a deep neural network framework for computing prices and deltas of American options in high dimensions.
Broadie, M. and Glasserman, P., Estimating security price derivatives using simulation. Management science
1996
Earlier work this paper cites.
Broadie, M. and Glasserman, P., Pricing American-style securities using simulation. J. Econom. Dynam. Control
1997
Earlier work this paper cites.
El Karoui, N., Peng, S. and Quenez, M.C., Backward stochastic differential equations in finance. Math. Finance
1997
Earlier work this paper cites.
Sola, J. and Sevilla, J., Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on nuclear science
1997
Earlier work this paper cites.
Tsitsiklis, J.N. and Van Roy, B., Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives. IEEE Trans. Automat. Control
1999
Earlier work this paper cites.
Heston, S. and Zhou, G., On the rate of convergence of discrete-time contingent claims. Math. Finance
2000
Earlier work this paper cites.
Longstaff, F.A. and Schwartz, E.S., Valuing American options by simulation: a simple least-squares approach. The review of financial studies
2001
Earlier work this paper cites.
Hull, J.C., Options futures and other derivatives
2003
Earlier work this paper cites.
Broadie, M., Glasserman, P. et al
2004
Earlier work this paper cites.
Bungartz, H.J. and Griebel, M., Sparse grids. Acta Numer
2004
Earlier work this paper cites.
Glasserman, P., Monte Carlo methods in financial engineering
2004
Earlier work this paper cites.
Haugh, M.B. and Kogan, L., Pricing American options: a duality approach. Oper. Res
2004
Earlier work this paper cites.
Stentoft, L., Convergence of the least squares Monte Carlo approach to American option valuation. Management Science
2004
Cited alongside, same era.
Achdou, Y. and Pironneau, O., Computational methods for option pricing
2005
Cited alongside, same era.
Firth, N.P., High dimensional American options. PhD thesis, University of Oxford, 2005
2005
Cited alongside, same era.
Duffy, D.J., Finite difference methods in financial engineering
2006
Cited alongside, same era.
He, C., Kennedy, J.S., Coleman, T.F., Forsyth, P.A., Li, Y. and Vetzal, K.R., Calibration and hedging under jump diffusion. Review of Derivatives Research
2006
Cited alongside, same era.
Reisinger, C. and Wittum, G., Efficient hierarchical approximation of high-dimensional option pricing problems. SIAM J. Sci. Comput
Murphy, K.P., Machine learning: a probabilistic perspective
2012
Later among the works it cites.
Reisinger, C. and Witte, J.H., On the use of policy iteration as an easy way of pricing American options. SIAM J. Financial Math
2012
Later among the works it cites.
Kingma, D.P. and Ba, J., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
2014
Later among the works it cites.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. et al
2016
Later among the works it cites.
Goodfellow, I., Bengio, Y. and Courville, A., Deep Learning
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2007
Cited alongside, same era.
Leentvaar, C.C.W., Pricing multi-asset options with sparse grids. , 2008
2008
Cited alongside, same era.
Kennedy, J.S., Forsyth, P.A. and Vetzal, K.R., Dynamic hedging under jump diffusion with transaction costs. Oper. Res
2009
Cited alongside, same era.
Thom, H., Longstaff Schwartz pricing of Bermudan options and their Greeks. , 2009
2009
Cited alongside, same era.
Kohler, M., A review on regression-based Monte Carlo methods for pricing American options. In Recent developments in applied probability and statistics
2010
Cited alongside, same era.
Kohler, M., Krzyżak, A. and Todorovic, N., Pricing of high-dimensional American options by neural networks. Math. Finance
2010
Cited alongside, same era.
Bouchard, B. and Warin, X., Monte-Carlo valuation of American options: facts and new algorithms to improve existing methods. In Numerical methods in finance
2012
Cited alongside, same era.
Beck, C., E, W. and Jentzen, A., Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. Journal of Nonlinear Science
2017
Later among the works it cites.
E, W., Han, J. and Jentzen, A., Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Commun. Math. Stat
2017
Later among the works it cites.
Forsyth, P., An introduction to computational finance without agonizing pain. , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
Han, J., Jentzen, A. and E, W., Solving high-dimensional partial differential equations using deep learning. Proc. Natl. Acad. Sci. USA
2018
Later among the works it cites.
Sirignano, J. and Spiliopoulos, K., DGM: A deep learning algorithm for solving partial differential equations. J. Comput. Phys
2018
Later among the works it cites.
Forsyth, P.A. and Vetzal, K.R., Quadratic convergence for valuing American options using a penalty method. SIAM J. Sci. Comput
2095
Closest in time.