Multivariate ARCH Tracking

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@hass-nation is already working on this.

Since Jun 27, 2026.

  • #854 by @hass-nation — open

Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
15/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
data

Research direction

Start by reviewing the staged roadmap in the issue and the linked pull request #854 to see which work is already underway. The issue names no files, tests, or entry points, so locating the relevant code will require repository research. Done would mean completing a clearly scoped stage or model from the plan, with tests; the full roadmap is broad and not a suitable first contribution.

Written by the indexing model from the issue text.

Description

enhancement

Long-term plan to implement some multivariate GARCH models:

First Stage:

  • Base classes
  • Constant Covariance volatility
  • Multivariate Normal distribution
  • Constant Mean
    Second Stage:
  • VAR-X mean model
  • VAR, LR, Constant Mean refactor, ZeroMean as special cases of VAR-X
    Third Stage:
  • Scalar BEKK with 1-step estimation
  • Scalar BEKK with 2-step estimation
  • Scalar BEKK with 3-step estimation (targetting)
  • Scalar BEKK with composite estimation (1, 2 and 3 step)
    Fourth Stage:
  • Multivariate Student's t distribution
    Fifth Stage:
  • Other BEKK and Matrix GARCH models
  • DCC (1-step and 2-step, full and composite) and cDCC (1-, 2- and 3-step, full and composite)
  • Other GARCH models (OGARCH, GO-GARCH??)
Dominant language
Python
Stars
1.6k
Forks
298
Avg merge
14h 46m
Merged PRs (30d)
106

Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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