jumpDiffusion-class: S4 class of model informations for the jump diffusion process

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Informations of model dY_t = b(φ,t,Y_t)dt + s(γ^2,t,Y_t)dW_t + h(θ,t,Y_t)dN_t with N_t\sim Pois(Λ(t, ξ)).

Slots

theta

parameter θ

phi

parameter φ

gamma2

parameter γ^2

xi

parameter ξ

b.fun

function b(φ,t,y)

s.fun

function s(γ^2,t,y)

h.fun

function b(θ,t,y)

Lambda

function Λ(t,ξ)

priorDensity

list of prior density functions, default is a non-informative approach

start

list of starting values for the Metropolis within Gibbs sampler

Examples

parameter <- list(phi = 0.01, theta = 0.1, gamma2 = 0.01, xi = c(2, 0.2))
b.fun <- function(phi, t, y) phi * y
s.fun <- function(gamma2, t, y) sqrt(gamma2) * y
h.fun <- function(theta, t, y) theta * y
Lambda <- function(t, xi) (t / xi[2])^xi[1]
priorDensity <- list(
  phi = function(phi) 1,
  theta = function(theta) dnorm(theta, 0.1, 0.001),
  gamma2 = function(gamma2) dgamma(1/gamma2, 3, 0.01*2),
  xi = function(xi) dgamma(xi, c(2, 0.2), 1)
)
start <- parameter
model <- set.to.class("jumpDiffusion", parameter, start = start,
  b.fun = b.fun, s.fun = s.fun, h.fun = h.fun, Lambda = Lambda,
  priorDensity = priorDensity)

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