摘要
There is a need for very fast option pricers when the financial objects are modeled by complex systems of stochastic differential equations.Here the authors investigate option pricers based on mixed Monte-Carlo partial differential solvers for stochastic volatility models such as Heston's.It is found that orders of magnitude in speed are gained on full Monte-Carlo algorithms by solving all equations but one by a Monte-Carlo method,and pricing the underlying asset by a partial differential equation with random coefficients,derived by Ito calculus.This strategy is investigated for vanilla options,barrier options and American options with stochastic volatilities and jumps optionally.
There is a need for very fast option pricers when the financial objects are modeled by complex systems of stochastic differential equations. Here the authors investigate option pricers based on mixed Monte-Carlo partial differential solvers for stochastic volatility models such as Heston＇s. It is found that orders of magnitude in speed are gained on full Monte-Carlo algorithms by solving all equations but one by a Monte-Carlo method, and pricing the underlying asset by a partial differential equation with random coefficients, derived by Ito calculus. This strategy is investigated for vanilla options, barrier options and American options with stochastic volatilities and jumps optionally.
