{
  "annotation_id": "f3c1bf55-b9cf-4a12-baa8-46e86e85e8eb",
  "date_created": "2026-03-02T18:01:10.121000Z",
  "date_modified": "2026-03-02T18:01:10.121000Z",
  "file_hash": "54b573478b9712d9f7bae4f7a8475a9fa7107526e63c3da047a000ea8e3ec26f",
  "private": false,
  "record": {
    "abstract": "Earlier we proposed the stochastic point process model, which reproduces a\nvariety of self-affine time series exhibiting power spectral density S(f)\nscaling as power of the frequency f and derived a stochastic differential\nequation with the same long range memory properties. Here we present a\nstochastic differential equation as a dynamical model of the observed memory in\nthe financial time series. The continuous stochastic process reproduces the\nstatistical properties of the trading activity and serves as a background model\nfor the modeling waiting time, return and volatility. Empirically observed\nstatistical properties: exponents of the power-law probability distributions\nand power spectral density of the long-range memory financial variables are\nreproduced with the same values of few model parameters.",
    "arxiv_id": "physics/0606115",
    "authors": [
      "V. Gontis",
      "B. Kaulakys"
    ],
    "categories": [
      "physics.soc-ph",
      "q-fin.ST"
    ],
    "doi": "10.1088/1742-5468/2006/10/P10016",
    "journal_ref": "J. Stat. Mech. (2006) P10016",
    "title": "Long-range memory model of trading activity and volatility",
    "url": "https://arxiv.org/abs/physics/0606115"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "aa4354e0-4baf-4a0b-9e7d-a84e3d908e5b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}