wARMASVp

Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models

Overview

wARMASVp provides estimation, simulation, hypothesis testing, and forecasting for univariate higher-order stochastic volatility SV(p) models. It supports Gaussian, Student-t, and Generalized Error Distribution (GED) innovations, with optional leverage effects.

The estimation method is based on closed-form Winsorized ARMA-SV (W-ARMA-SV) moment-based estimators that avoid numerical optimization, making them fast and reliable.

Installation

You can install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("roga11/wARMASVp")

Features

• Estimation: SV(p) models with Gaussian, Student-t, or GED errors via svp()
• Leverage effects: Asymmetric volatility estimation for Gaussian SV(p)
• Simulation: Generate SV(p) data with sim_svp()
• Hypothesis testing: LMC and MMC procedures for autoregressive order, leverage, and heavy tails
• Forecasting: Kalman filter-based h-step-ahead volatility forecasts via forecast_svp()
• Standard errors: Simulation-based confidence intervals via svpSE()

Quick Start

library(wARMASVp)

# Simulate Gaussian SV(1)
y <- sim_svp(1000, phi = 0.95, sigy = 1, sigv = 0.3)

# Estimate
fit <- svp(y, p = 1)
summary(fit)

# Standard errors
se <- svpSE(fit, n_sim = 99)
se$CI

# Forecast
fc <- forecast_svp(fit, H = 10)
plot(fc)

References

• Ahsan, N. and Dufour, J.-M. (2021). Simple estimators and inference for higher-order stochastic volatility models. Journal of Econometrics, 224(1), 181-197. doi:10.1016/j.jeconom.2020.01.018

• Ahsan, N., Dufour, J.-M., and Rodriguez Rondon, G. (2025). Estimation and testing for higher-order stochastic volatility models with leverage and heavy tails. Journal of Time Series Analysis.

• Ahsan, N., Dufour, J.-M., and Rodriguez Rondon, G. (2026). wARMASVp: An R package for higher-order stochastic volatility models. Working paper.

License

GPL (>= 3)