) multivariate volatility models, both in-sample and out-of-sample. We also comment on the possibility to use composite likelihood …

We present a new model to decompose total daily return volatility into a filtered (high-frequency based) open …-to-close volatility and a time-varying scaling factor. We use score-driven dynamics based on fat-tailed distributions to limit the impact …-to-close volatility changes substantially through time, especially for financial stocks. …

autoregressive conditional heteroskedasticity model and the dynamic conditional correlation model where distributional assumptions …

-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation …

We introduce a new, easily scalable model for dynamic conditional correlation matrices based on a recursion of dynamic … bivariate partial correlation models. By exploiting the model's recursive structure and the theory of perturbed stochastic …