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).