Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models R package wARMASVp version 0.2.0 Estimation, simulation, hypothesis testing, AR-order selection, and forecasting for univariate higher-order stochastic volatility SV(p) models. Supports Gaussian, Student-t, and Generalized Error Distribution (GED) innovations, with optional leverage effects. Estimation uses closed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based methods that avoid numerical optimization. Hypothesis testing includes Local Monte Carlo (LMC) and Maximized Monte Carlo (MMC) procedures for leverage effects, heavy tails, and autoregressive order. AR-order selection is also available via information criteria (BIC/AIC) using the Kalman-filter quasi-likelihood and the Hannan-Rissanen ARMA residual variance. Forecasting is based on Kalman filtering and smoothing. See Ahsan and Dufour (2021) <doi:10.1016/j.jeconom.2021.03.008>, Ahsan, Dufour, and Rodriguez-Rondon (2025) <doi:10.1111/jtsa.12851>, and Ahsan, Dufour, and Rodriguez-Rondon (2026) <doi:10.34989/swp-2026-8> for details. Software Imports: Rcpp (>= 1.0.0), gsignal, stats LinkingTo: Rcpp, RcppArmadillo Suggests: pso, GenSA, testthat (>= 3.0.0), knitr, rmarkdown Gabriel Rodriguez-Rondon <gabriel.rodriguezrondon@mail.mcgill.ca> Comprehensive R Archive Network (CRAN) Gabriel Rodriguez-Rondon [aut, cre] (ORCID: <https://orcid.org/0009-0005-3769-9921>), Md. Nazmul Ahsan [aut], Jean-Marie Dufour [aut] GPL (>= 3) 2026-05-15 application/tgz https://CRAN.R-project.org/package=wARMASVp doi:10.32614/CRAN.package.wARMASVp