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wARMASVp 0.2.0

New features

• svp_IC() and svp_AR_order(): AR-order selection for SV(p) models via information criteria. Four criteria are returned by default (BIC_Kalman, AIC_Kalman, BIC_HR, AIC_HR), spanning state-space QML and Hannan-Rissanen estimation families; four more (AICc_Kalman, BIC_Whittle, BIC_YW, AIC_YW) are available opt-in via the criteria argument. svp_AR_order() sweeps over p = 1, ..., pmax; both functions read errorType and leverage from the fitted model.
• lmc_ar() / mmc_ar() now accept errorType = "Gaussian", "Student-t", or "GED". The tail parameter is held fixed at the null MLE during simulation; innovations are pre-drawn from the corresponding distribution.

Breaking changes

• sim_svp() now always returns a named list list(y, h, z, v) of length-n vectors (observed returns, log-volatility path, return innovation, volatility innovation). The K (multiple-replicate) argument has been removed; wrap the call in a loop for replicates. Callers that previously relied on sim_svp() returning a bare vector must now extract $y.

Defaults

• filter_svp() and forecast_svp() gain a proxy argument and now default to proxy = "bayes_optimal" (was the paper-faithful "u"-proxy). For Student-t leverage this uses the posterior mean E[zeta | u] rather than the raw u-proxy, which has marginal variance nu/(nu-2) > 1. No effect for Gaussian, GED, or non-leverage models.

Bug fixes

• GMKF: corrected the Student-t leverage parameterization in the Gaussian mixture Kalman filter.
• Filtering / forecasting: corrected the state-innovation variance Q under leverage. The filter uses the conditional Q = sigma_v^2 (1 - delta^2); the forecaster uses the conditional Q at horizon 1 and the marginal Q = sigma_v^2 at horizons >= 2.
• Bootstrap particle filter: Student-t leverage recovery now samples the mixing variable from its posterior rather than its prior.
• GED leverage: the CKF/GMKF leverage shift now applies the copula proxy rather than using the raw innovation.
• MMC: the observed test statistic S0 is kept fixed during optimization, per Dufour (2006, eq. 4.22). Previously recomputed at each optimizer evaluation in the leverage, Student-t, and GED tests.
• MMC: default eps[sigma_y] = 0 in all MMC functions (was 0.3). The simulated null distribution is sigma_y-invariant, so varying it is unnecessary.

Performance

• The KSC mixture EM step used by the GMKF (fit_ksc_mixture()) is now implemented in C++, giving roughly a 12x speedup for Student-t and GED filtering.

Documentation

• DESCRIPTION: added the DOI for the JTSA 2025 reference per CRAN reviewer feedback.
• Updated the introductory vignette with an AR-order-selection section.

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 2026). 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.