bmgarch

Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) doi:10.1198/073500102288618487 and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) doi:10.1017/S0266466600009063 while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) doi:10.31234/osf.io/j57pk. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.

Last 30 days

This package has been downloaded 334 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 15 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 4,625 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jan 30, 2025 with 53 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.