A stochastic volatility model for volatility asymmetry and propagation

In this paper, we propose a novel asymmetric stochastic volatility model that uses a heterogeneous autoregressive process to capture the persistence and decay of volatility asymmetry over time, which is different from traditional approaches. We analyze the properties of the model in terms of volatility asymmetry and propagation using a recently introduced concept in the field and find that the new model can generate both volatility asymmetry and propagation effects. We also introduce Data Cloning for parameter estimation, which provides robustness and computational efficiency compared to conventional techniques. Our empirical analysis shows that the new proposal outperforms a recent competitor in terms of in-sample fit and out-of-sample volatility prediction across different financial return series, making it a more effective tool for capturing the dynamics of volatility asymmetry in financial markets.