{
  "annotation_id": "95f0c07f-663b-4ee6-9d42-45b8f88a69a7",
  "date_created": "2026-03-02T18:01:18.545000Z",
  "date_modified": "2026-03-02T18:01:18.545000Z",
  "file_hash": "cdf7cd2a3b42a0181d2b69071bedbeba6b1b4ab184235e569c3a0008e18213eb",
  "private": false,
  "record": {
    "abstract": "The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the\ngeneralized autoregressive conditional heteroscedasticity (GARCH) model. The\nHMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it\nupdates all parameters at once. We demonstrate that how the HMC reproduces the\nGARCH parameters correctly. The algorithm is rather general and it can be\napplied to other models like stochastic volatility models.",
    "arxiv_id": "physics/0702240",
    "authors": [
      "Tetsuya Takaishi"
    ],
    "categories": [
      "physics.comp-ph",
      "physics.data-an",
      "q-fin.ST"
    ],
    "doi": "10.2991/jcis.2006.159",
    "journal_ref": "Proceedings of the 9th Joint Conference on Information Sciences\n  2006, CIEF-214",
    "title": "Bayesian estimation of GARCH model by hybrid Monte Carlo",
    "url": "https://arxiv.org/abs/physics/0702240"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a97177cf-4345-4e98-a36c-d45d072c0568",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}