estimate_parameters: Estimate parameters for the stochastic volatility model

In stochvolTMB: Likelihood Estimation of Stochastic Volatility Models

Description

Estimate parameters of a stochastic volatility model with a latent log-volatility following an autoregressive process of order one with normally distributed noise. The following distributions are implemented for the observed process:

• Gaussian distribution

• t-distribution

• Leverage: Gaussian distribution with leverage where the noise of the latent process is correlated with the observational distribution

• Skew gaussian distribution

The parameters is estimated by minimizing the negative log-likelihood (nll) and the latent log-volatility is integrated out by applying the Laplace approximation.

Usage

estimate_parameters(data, model = "gaussian", opt.control = NULL, ...)

Arguments

data	A vector of observations.
model	A character specifying the model. Must be one of the following: "gaussian", "t", "leverage", "skew_gaussian".
opt.control	An optional list of parameters for nlminb.
...	additional arguments passed to MakeADFun.

Value

Object of class stochvolTMB

Examples

# load data
data("spy")

# estimate parameters 
opt <- estimate_parameters(spy$log_return, model = "gaussian")

# get parameter estimates with standard error
estimates <- summary(opt)

# plot estimated volatility with 95 % confidence interval
plot(opt, include_ci = TRUE)

stochvolTMB documentation built on Aug. 13, 2021, 5:07 p.m.