R/feynman_kac.R

In shill1729/FeynmanKacSolver: Feynman-Kac PDE solver for Ito diffusions and processes

#' Compute conditional expectations and optimally stopped conditional expectations via PDEs, PIDEs, and variational inequalities
#'
#' @param K the parameter for the payoff functions
#' @param payoff string either "cdf", "put", "call" or "straddle" (the payoff functions with one parameter)
#' @param setup output from either \code{pde_setup} or \code{pide_setup}
#' @param variational boolean TRUE for variational inequalities or FALSE for plain PDEs or PIDEs
#' @param exponential boolean to exponentiate the input to the payoff function or not
#'
#' @description {Vectorized solver for PDE, PIDE and variational inequality problems over the parameter for the terminal payoff function.}
#' @return vector
#' @export feynman_kac
feynman_kac <- function(K, payoff, setup, variational = FALSE, exponential = FALSE)
{
  payoff_name_call <- paste("payoff_", payoff, sep = "")


  nk <- length(K)
  problem_type <- setup$type
  output <- matrix(0, nrow = nk)
  for(i in 1:nk)
  {
    if(exponential)
    {
      payoff <- function(y) do.call(what = payoff_name_call, args = list(x = setup$spot*exp(y), K = K[i]))
    } else
    {
      payoff <- function(y) do.call(what = payoff_name_call, args = list(x = y, K = K[i]))
    }

    payoff <- grid_function(payoff, x = setup$grid.x)
    setup$payoff <- payoff
    if(problem_type == "pde")
    {

      output[i] <- pde_solve(setup, variational, output = "price")
    } else if(problem_type == "pide")
    {
      output[i] <- pide_solve(setup, variational, output = "price")
    }
  }
  return(data.frame(K = K, u = output))
}