bssm (version 0.1.8)

svm: Stochastic Volatility Model

Description

Constructs a simple stochastic volatility model with Gaussian errors and first order autoregressive signal.

Usage

svm(y, rho, sd_ar, sigma, mu)

Arguments

y

Vector or a ts object of observations.

rho

prior for autoregressive coefficient.

sd_ar

Prior for the standard deviation of noise of the AR-process.

sigma

Prior for sigma parameter of observation equation.

mu

Prior for mu parameter of transition equation. Ignored if sigma is provided.

Value

Object of class svm or svm2.

Examples

# NOT RUN {
data("exchange")
exchange <- exchange[1:100] # faster CRAN check
model <- svm(exchange, rho = uniform(0.98,-0.999,0.999),
 sd_ar = halfnormal(0.15, 5), sigma = halfnormal(0.6, 2))

obj <- function(pars) {
   -logLik(svm(exchange, rho = uniform(pars[1],-0.999,0.999),
   sd_ar = halfnormal(pars[2],sd=5),
   sigma = halfnormal(pars[3],sd=2)), nsim_states = 0)
}
opt <- nlminb(c(0.98, 0.15, 0.6), obj, lower = c(-0.999, 1e-4, 1e-4), upper = c(0.999,10,10))
pars <- opt$par
model <- svm(exchange, rho = uniform(pars[1],-0.999,0.999),
  sd_ar = halfnormal(pars[2],sd=5),
  sigma = halfnormal(pars[3],sd=2))

# }