{
  "annotation_id": "06992169-b47f-450a-aeff-546ce6449112",
  "date_created": "2026-03-02T18:01:11.528000Z",
  "date_modified": "2026-03-02T18:01:11.528000Z",
  "file_hash": "5eab307f6b7eec59b1ebc04706d598e54c02083c42aa4c163308206eb1bb3160",
  "private": false,
  "record": {
    "abstract": "We use a replica approach to deal with portfolio optimization problems. A\ngiven risk measure is minimized using empirical estimates of asset values\ncorrelations. We study the phase transition which happens when the time series\nis too short with respect to the size of the portfolio. We also study the noise\nsensitivity of portfolio allocation when this transition is approached. We\nconsider explicitely the cases where the absolute deviation and the conditional\nvalue-at-risk are chosen as a risk measure. We show how the replica method can\nstudy a wide range of risk measures, and deal with various types of time series\ncorrelations, including realistic ones with volatility clustering.",
    "arxiv_id": "physics/0608035",
    "authors": [
      "Stefano Ciliberti",
      "Marc Mezard"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.dis-nn",
      "q-fin.RM"
    ],
    "doi": "10.1140/epjb/e2007-00130-7",
    "title": "Risk Minimization through Portfolio Replication",
    "url": "https://arxiv.org/abs/physics/0608035"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "826d3f79-b7e1-481a-b04c-b609e61f3963",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}