sampleLogVols: Sample the latent log-volatilities

In drkowal/dsp: Dynamic Shrinkage Processes

Sample the latent log-volatilities

Description

Compute one draw of the log-volatilities using a discrete mixture of Gaussians approximation to the likelihood (see Omori, Chib, Shephard, and Nakajima, 2007) where the log-vols are assumed to follow an AR(1) model with time-dependent innovation variances. More generally, the code operates for p independent AR(1) log-vol processes to produce an efficient joint sampler in O(Tp) time.

Usage

sampleLogVols(h_y, h_prev, h_mu, h_phi, h_sigma_eta_t, h_sigma_eta_0)

Arguments

h_y	the T x p matrix of data, which follow independent SV models
h_prev	the T x p matrix of the previous log-vols
h_mu	the p x 1 vector of log-vol unconditional means
h_phi	the p x 1 vector of log-vol AR(1) coefficients
h_sigma_eta_t	the T x p matrix of log-vol innovation standard deviations
h_sigma_eta_0	the p x 1 vector of initial log-vol innovation standard deviations

Value

T x p matrix of simulated log-vols

Note

For Bayesian trend filtering, p = 1. More generally, the sampler allows for p > 1 but assumes (contemporaneous) independence across the log-vols for j = 1,...,p.

drkowal/dsp documentation built on July 19, 2023, 11:42 a.m.