Changes in version 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.

                 Changes in version 0.1.0 (2026-04-22)                  

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).