simulate-jumpDiffusion-method: Simulation of jump diffusion process

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Simulation of jump diffusion process dY_t = b(φ,t,Y_t)dt + s(γ,t,Y_t)dW_t + h(η,t,Y_t)dN_t.

Usage

## S4 method for signature 'jumpDiffusion'
simulate(object, nsim = 1, seed = NULL, t, y0,
  start = c(0, 0), mw = 1, plot.series = TRUE)

Arguments

object	class object of parameters: "jumpDiffusion"
nsim	number of trajectories to simulate. Default is 1.
seed	optional: seed number for random number generator
t	vector of time points
y0	starting point of process
start	vector: start[1] starting point time, start[2] starting point for Poisson process
mw	mesh width for finer Euler approximation to simulate time-continuity
plot.series	logical(1), if TRUE, simulated series are depicted grafically

Examples

model <- set.to.class("jumpDiffusion",
   parameter = list(theta = 0.1, phi = 0.05, gamma2 = 0.1, xi = c(3, 1/4)),
   Lambda = function(t, xi) (t/xi[2])^xi[1])
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, y0 = 0.5)

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.