wARMASVp
Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models
Estimation, simulation, hypothesis testing, 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 selection. Forecasting is based on Kalman filtering and smoothing. See Ahsan and Dufour (2021) <doi:10.1016/j.jeconom.2020.01.018>, Ahsan, Dufour, and Rodriguez Rondon (2025) for details.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | wARMASVp_0.1.0.tar.gz |
675.0 KiB |
0.1.0 |
rolling linux/noble R-4.5 | wARMASVp_0.1.0.tar.gz |
690.1 KiB |
0.1.0 |
rolling source/ R- | wARMASVp_0.1.0.tar.gz |
213.4 KiB |
0.1.0 |
latest linux/jammy R-4.5 | wARMASVp_0.1.0.tar.gz |
675.0 KiB |
0.1.0 |
latest linux/noble R-4.5 | wARMASVp_0.1.0.tar.gz |
690.1 KiB |
0.1.0 |
latest source/ R- | wARMASVp_0.1.0.tar.gz |
213.4 KiB |
0.1.0 |
2026-04-26 source/ R- | wARMASVp_0.1.0.tar.gz |
213.4 KiB |
0.1.0 |
2026-04-23 source/ R- | wARMASVp_0.1.0.tar.gz |
213.4 KiB |