svc_dB: Extract the Brownian increments from the SVC model.

In mlysy/svcommon: Fast Inference for Common-Factor Stochastic Volatility Models

Extract the Brownian increments from the SVC model.

Description

Extract the Brownian increments from the SVC model.

Usage

svc_dB(Xt, log_VPt, log_Vt, dt, alpha, log_gamma, mu, log_sigma, logit_rho)

Arguments

Xt	Matrix of ⁠nobs x (nasset + 1)⁠ asset log prices, where the first column is that of the asset common-factor proxy.
log_VPt	Vector of nobs volatility proxy values on the log standard deviation scale. See 'Details'.
log_Vt	Optional vector of ⁠nobs x (nasset + 1)⁠ volatilities on the log standard deviation scale. See 'Details'.
dt	Interobservation time.
alpha	Optional vector of (nasset + 1) asset growth rate parameters. See 'Details'.
log_gamma	Optional vector of (nasset + 2) log-volatility mean reversion parameters on the log scale. The first two correspond to the volatility proxy and the common-factor asset's volatility, respectively. See 'Details'.
mu	Optional vector of (nasset + 2) log-volatility mean parameters. See 'Details'.
log_sigma	Optional vector of (nasset + 2) log-volatility diffusion parameters on the log scale. See 'Details'.
logit_rho	Optional vector of (nasset + 1) correlation parameters between asset and volatility innovations, on the logit scale. The first one is that of the common-factor asset proxy. See 'Details'.

Value

A list with elements:

V

An ⁠nobs x (nasset+2)⁠ matrix of the log-volatility innovations.

X

An ⁠nobs x (nasset+1)⁠ matrix of log-asset innovations.

Z

An ⁠nobs x (nasset+1)⁠ matrix of residual log-asset innovations, after account for the log-volatility innovations. That is, ⁠dB_Z = (dB_X - rho dB_V) / sqrt(1 - rho^2).⁠

mlysy/svcommon documentation built on Sept. 15, 2024, 1:15 a.m.