wARMASVp 0.1.0
Initial release.
Estimation
svp(): Closed-form W-ARMA-SV estimation for SV(p)
models of any order.- Gaussian, Student-t, and GED innovation distributions supported for
all p.
- Leverage estimation for all distributions: closed-form for Gaussian
and Student-t, exact root-finding for GED.
svpSE(): Simulation-based standard errors and
confidence intervals.
Simulation
sim_svp(): Simulate SV(p) processes with Gaussian,
Student-t, or GED innovations, with optional leverage effects for all
distributions.
Hypothesis Testing
- Local Monte Carlo (LMC) and Maximized Monte Carlo (MMC) tests based
on Dufour (2006), with fixed-innovation MMC for exact finite-sample
inference:
lmc_ar() / mmc_ar(): AR order
selection.lmc_lev() / mmc_lev(): Leverage effects
(all distributions).lmc_t() / mmc_t(): Student-t vs. Gaussian
(with directional testing).lmc_ged() / mmc_ged(): GED vs. Gaussian
(with directional testing).
- All test procedures support general SV(p) (any order).
Filtering
filter_svp(): Kalman filtering and smoothing with three
methods:
- Corrected Kalman Filter (CKF): Gaussian approximation, fast.
- Gaussian Mixture Kalman Filter (GMKF): KSC (1998) 7-component
mixture, recommended.
- Bootstrap Particle Filter (BPF): exact density weights,
benchmark.
Forecasting
forecast_svp(): Multi-step ahead volatility forecasts
with MSE-based confidence bands. Supports log-variance, variance, and
volatility output scales.
Convention Changes
- Switched Student-t innovations from standardized (unit variance) to
unstandardized (raw t(nu) with Var = nu/(nu-2)), matching the SV-t
literature (Chib, Nardari & Shephard 2002; Jacquier, Polson &
Rossi 2004) and the SVHT reference paper (Ahsan, Dufour & Rodriguez
Rondon 2025b). The mean-of-log-squared formula is now:
mu_bar(nu) = psi(1/2) - psi(nu/2) + log(nu). Simulation no
longer divides raw Student-t samples by sqrt(nu/(nu-2)). GED innovations
remain standardized (unit variance), following Nelson (1991).