eou_sim: Simulate time series from the exponential Ornstein-Uhlenbeck...

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

Simulate time series from the exponential Ornstein-Uhlenbeck stochastic volatility model.

Description

Simulate time series from the exponential Ornstein-Uhlenbeck stochastic volatility model.

Usage

eou_sim(nobs, dt, X0, log_V0, alpha, log_gamma, mu, log_sigma, logit_rho, dBt)

Arguments

nobs	Length of time series.
dt	Interobservation time.
X0	Scalar or vector of nseries asset log prices at time t = 0.
log_V0	Scalar or vector of nseries volatilities at time t = 0, on the log standard deviation scale.
alpha	Scalar or vector of nseries growth rate parameters.
log_gamma	Scalar or vector of nseries log-volatility mean reversion parameters on the log scale.
mu	Scalar or vector of nseries log-volatility mean parameters.
log_sigma	Scalar or vector of nseries log-volatility diffusion parameters on the log scale.
logit_rho	Scalar or vector of nseries correlation parameters between asset and volatility innovations, on the logit scale.
dBt	An optional list with elements V and Z corresponding to matrices of size ⁠nobs x nseries⁠ of pre-specified Brownian innovations. If missing these consist of iid draws from an N(0, dt) distribution.

Value

A list containing matrices Xt and log_Vt of ⁠nobs x nseries⁠ of eOU observations, where each column corresponds to a process observed at times ⁠t = dt, 2dt, ..., nobs*dt⁠.

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