Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers.
Adaptive Multilevel Splitting (AMS) simulation tools for rare-event option pricing and path generation in continuous-time models.
Core routines are implemented in C++ (Rcpp/RcppArmadillo) for speed.
Status: under active development; preparing for CRAN submission.
Install the development version from GitHub:
install.packages("remotes") remotes::install_github("RiccardoGozzo/amsSim")
devtools::install_github("RiccardoGozzo/amsSim")
pak::pak("RiccardoGozzo/amsSim")
library(amsSim)
set.seed(1) res <- simulate_AMS(1, n = 500, t = 1, p = 252, r = 0.03, sigma = 0.2, S0 = 1, rho = NULL) str(res)
set.seed(1) out <- AMS(model = 2, type = 3, funz = 1, n = 500, t = 1, p = 252, r = 0.03, sigma = 0.2, rho = -0.5, S0 = 1, rim = 0, Lmax = 0.5, strike = 1.3, K = 200) str(out)
Returns simulated paths: model 1 (Black–Scholes): matrix/list with S of size n x (p - rim + 1) Heston models (2–4): list with S, V
Runs the adaptive splitting loop and returns list(price, std).
Contributions are welcome!
If you wish to contribute, please:
This package is released under the MIT License.
See the file LICENSE for details.
If you use amsSim in your research, please cite it as follows:
Gozzo, R. (2025). Adaptive Multilevel Splitting: First Application to Rare-Event Derivative Pricing Available at: https://arxiv.org/html/2510.23461v1