We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional … involving U.S. inflation we find that these moving average stochastic volatility models provide better in sample fitness and out …-of-sample forecast performance than the standard variants with only stochastic volatility …

The rough path-dependent volatility (RPDV) model (Parent 2022) effectively captures key empirical features that are … characteristic of volatility dynamics, making it a suitable choice for volatility forecasting. However, its complex structure … the RPDV model a competitive tool for volatility forecasting.To achieve this objective, the article proposes an innovative …

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat …-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time …

volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross …

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily … heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility … supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast …