{
  "annotation_id": "c34b6e5c-bb59-45ac-8f97-62d2707c92e4",
  "date_created": "2026-03-02T18:01:14.821000Z",
  "date_modified": "2026-03-02T18:01:14.821000Z",
  "file_hash": "b8a8d897b0f27291edf17deec725b9e51d24c17fce1f6fd46dc705a9f54e6ad4",
  "private": false,
  "record": {
    "abstract": "A parameterization that is a modified version of a previous work is proposed\nfor the returns and correlation matrix of financial time series and its\nproperties are studied. This parameterization allows easy introduction of\nnon-stationarity and it shows several of the characteristics of the true,\nobserved realizations, such as fat tails, volatility clustering, and a spectrum\nof eigenvalues of the correlation matrix that can be understood as an extension\nof Random Matrix Theory results. The predicted behavior of this\nparameterization for the eigenvalues is compared with the eigenvalues of\nBrazilian assets and it is shown that those predictions fit the data better\nthan Random Matrix Theory.",
    "arxiv_id": "physics/0701025",
    "authors": [
      "Andre C. R. Martins"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.data-an",
      "q-fin.ST"
    ],
    "doi": "10.1016/j.physa.2007.02.108",
    "journal_ref": "Physica A, 383, pp. 527-532 (2007)",
    "title": "Random, but not so much: A parameterization for the returns and   correlation matrix of financial time series",
    "url": "https://arxiv.org/abs/physics/0701025"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "70dade8d-7414-4281-a2fd-c1e34ae5e1bf",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}