NEWS

wARMASVp 0.2.0

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.

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.

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.

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.

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.

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

Initial release.

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.

sim_svp(): Simulate SV(p) processes with Gaussian, Student-t, or GED innovations, with optional leverage effects for all distributions.

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).

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.

forecast_svp(): Multi-step ahead volatility forecasts with MSE-based confidence bands. Supports log-variance, variance, and volatility output scales.

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).