Before revision:  Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models , such as GARCH and its variants, and the stochastic MCMC-based models , and the Gaussian-process-based, on the  average negative log-likelihood measure .
After revision:  Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models , such as GARCH and its variants, and the stochastic MCMC-based models , and the Gaussian-process-based, on the as well as the Gaussian process volatility model average negative log-likelihood measure .