Pricing of path-dependent American options by Monte Carlo simulation

2007 ◽  
Vol 31 (11) ◽  
pp. 3478-3502 ◽  
Author(s):  
Hajime Fujiwara ◽  
Masaaki Kijima
2021 ◽  
Vol 69 (1) ◽  
pp. 1-6
Author(s):  
SM Arif Hossen ◽  
ABM Shahadat Hossain

The main purpose of this dissertation is to study Monte Carlo (MC) and Quasi-Monte Carlo (QMC) methods for pricing financial derivatives. We estimate the Price of European as well as various path dependent options like Asian, Barrier and American options by using these methods. We also compute the numerical results by the above mentioned methods and compare them graphically as well with the help of the MATLAB Coding. Dhaka Univ. J. Sci. 69(1): 1-6, 2021 (January)


2004 ◽  
Vol 07 (05) ◽  
pp. 591-614 ◽  
Author(s):  
G. N. MILSTEIN ◽  
O. REIß ◽  
J. SCHOENMAKERS

We introduce a new Monte Carlo method for constructing the exercise boundary of an American option in a generalized Black–Scholes framework. Based on a known exercise boundary, it is shown how to price and hedge the American option by Monte Carlo simulation of suitable probabilistic representations in connection with the respective parabolic boundary value problem. The method presented is supported by numerical experiments.


2021 ◽  
Author(s):  
Alejandro Rojas-Bernal ◽  
Mauricio Villamizar-Villegas

We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.


1997 ◽  
Vol 43 (11) ◽  
pp. 1589-1602 ◽  
Author(s):  
Dwight Grant ◽  
Gautam Vora ◽  
David Weeks

2016 ◽  
Vol 6 (3) ◽  
pp. 314-336 ◽  
Author(s):  
Minseok Park ◽  
Kyungsub Lee ◽  
Geon Ho Choe

AbstractWe introduce a new method to compute the approximate distribution of the Delta-hedging error for a path-dependent option, and calculate its value over various strike prices via a recursive relation and numerical integration. Including geometric Brownian motion and Merton's jump diffusion model, we obtain the approximate distribution of the Delta-hedging error by differentiating its price with respect to the strike price. The distribution from Monte Carlo simulation is compared with that obtained by our method.


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