Winsorized ARMA Estimation for Higher-Order Stochastic Volatility Models
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.
You can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("roga11/wARMASVp")- 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()
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)-
Ahsan, M. 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.2021.03.008
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Ahsan, M. N., Dufour, J.-M., and Rodriguez-Rondon, G. (2025). Estimation and inference for higher-order stochastic volatility models with leverage. Journal of Time Series Analysis, 46(6), 1064-1084. doi:10.1111/jtsa.12851
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Ahsan, M. N., Dufour, J.-M., and Rodriguez-Rondon, G. (2026). Estimation and inference for stochastic volatility models with heavy-tailed distributions. Bank of Canada Staff Working Paper 2026-8. doi:10.34989/swp-2026-8
GPL (>= 3)