Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models // 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.