BaPreStoPro (version 0.1)

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

Description

Informations of model $dY_t = b(\phi,t,Y_t)dt + s(\gamma^2,t,Y_t)dW_t + h(\theta,t,Y_t)dN_t$ with $N_t\sim Pois(\Lambda(t, \xi))$.

Slots

theta

parameter $\theta$

phi

parameter $\phi$

gamma2

parameter $\gamma^2$

xi

parameter $\xi$

b.fun

function $b(\phi,t,y)$

s.fun

function $s(\gamma^2,t,y)$

h.fun

function $b(\theta,t,y)$

Lambda

function $\Lambda(t,\xi)$

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)