  The heterogeneous autoregressive (HAR) model is revised by modeling the joint
distribution of the four partial-volatility terms therein involved. Namely,
today's, yesterday's, last week's and last month's volatility components. The
joint distribution relies on a (C-) Vine copula construction, allowing to
conveniently extract volatility forecasts based on the conditional expectation
of today's volatility given its past terms. The proposed empirical application
involves more than seven years of high-frequency transaction prices for ten
stocks and evaluates the in-sample, out-of-sample and one-step-ahead forecast
performance of our model for daily realized-kernel measures. The model proposed
in this paper is shown to outperform the HAR counterpart under different models
for marginal distributions, copula construction methods, and forecasting
settings.
