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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).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.