On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters
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Harold A. Lay
,
Zane Colgin
Abstract
We explore different methods of solving systems of stochastic differential equations by first implementing the Euler–Maruyama and Milstein methods with a Monte Carlo simulation on a CPU. The performance of the methods is significantly improved through the recently developed antithetic multilevel Monte Carlo estimator, which yields a computation complexity of
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© 2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Nesting Monte Carlo for high-dimensional non-linear PDEs
- Strong rate of convergence for the Euler–Maruyama approximation of one-dimensional stochastic differential equations involving the local time at point zero
- Global sensitivity analysis for a stochastic flow problem
- Sampling from the 𝒢I0 distribution
- A second-order weak approximation of SDEs using a Markov chain without Lévy area simulation
- On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters
- Random walk algorithms for elliptic equations and boundary singularities
Artikel in diesem Heft
- Frontmatter
- Nesting Monte Carlo for high-dimensional non-linear PDEs
- Strong rate of convergence for the Euler–Maruyama approximation of one-dimensional stochastic differential equations involving the local time at point zero
- Global sensitivity analysis for a stochastic flow problem
- Sampling from the 𝒢I0 distribution
- A second-order weak approximation of SDEs using a Markov chain without Lévy area simulation
- On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters
- Random walk algorithms for elliptic equations and boundary singularities
