{
  "annotation_id": "61b70db3-0f2e-4602-86da-08a253956621",
  "date_created": "2026-03-02T18:01:07.885000Z",
  "date_modified": "2026-03-02T18:01:07.885000Z",
  "file_hash": "c7b0038a5e79d787289bbd7b241183b47a910c66073bac71ea65237c0f51b9ce",
  "private": false,
  "record": {
    "abstract": "Detrended fluctuation analysis (DFA) [1] of the volatility series has been\nfound to be useful in dentifying possible nonlinear/multifractal dynamics in\nthe empirical sample [2-4]. Long-range volatile correlation can be an outcome\nof static as well as dynamical nonlinearity. In order to argue in favor of\ndynamical nonlinearity, surrogate testing is used in conjunction with\nvolatility analysis [2-4]. In this brief communication, surrogate testing of\nvolatility series from long-range correlated noise and their static, invertible\nnonlinear transforms is investigated. Long-range correlated monofractal noise\nis generated using FARIMA (0, d, 0) with Gaussian and non-Gaussian innovations.\nWe show significant deviation in the scaling behavior between the empirical\nsample and the surrogate counterpart at large time-scales in the case of FARIMA\n(0, d, 0) with non-Gaussian innovations whereas no such discrepancy was\nobserved in the case of Gaussian innovations. The results encourage cautious\ninterpretation of surrogate testing in the presence of non-Gaussian\ninnovations.",
    "arxiv_id": "physics/0603126",
    "authors": [
      "Radhakrishnan Nagarajan"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1016/j.physa.2006.07.027",
    "title": "Surrogate testing of volatility series from long-range correlated noise",
    "url": "https://arxiv.org/abs/physics/0603126"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "132290f4-ca6e-4959-9032-7bc390549ce7",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}