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In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms.
J. F. Carriere, Valuation of the early-exercise price for options using simulations and nonparametric regression, Insurance: mathematics and Economics 19 (1) (1996) 19–30
1996
Earlier work this paper cites.
M. Broadie, P. Glasserman, Pricing american-style securities using simulation, Journal of Economic Dynamics and Control 21 (8) (1997) 1323–1352
1997
Earlier work this paper cites.
L. B. Andersen, A simple approach to the pricing of bermudan swaptions in the multi-factor libor market model, Journal of Computational Finance 3 (1999) 5–32
1999
Earlier work this paper cites.
F. Longstaff, E. Schwartz, Valuing american options by simulation: a simple least-squares approach., Review of Financial Studies 14 (1) (2001) 113–147
2001
Cited alongside, same era.
J. Tsitsiklis, B. Van Roy, Regression methods for pricing complex american style options., IEEE Trans. Neural. Net. 12 (14) (2001) 694–703
2001
Cited alongside, same era.
P. Glasserman, Monte Carlo methods in financial engineering, Vol. 53, Springer Science & Business Media, 2003
2003
Cited alongside, same era.
S. Becker, P. Cheridito, A. Jentzen, Deep optimal stopping, arXiv preprint arXiv:1804.05394
Cited in the paper.
D. Egloff, et al., Monte carlo algorithms for optimal stopping and statistical learning, The Annals of Applied Probability 15 (2) (2005) 1396–1432
2005
Later among the works it cites.
D. Belomestny, Pricing bermudan options by nonparametric regression: optimal rates of convergence for lower estimates, Finance and Stochastics 15 (4) (2011) 655–683
2011
Later among the works it cites.
2011
Later among the works it cites.
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