{
  "abstract": "Forecasting a correlation or covariance matrix is common in risk management and portfolio construction, but evaluating such a forecast correctly is not routine: naive matrix-comparison metrics are not proper scoring rules, walk-forward evaluation windows are easy to overlap with the estimation window in ways that silently leak information, and significance testing on serially dependent forecast-error sequences needs machinery few analysts implement from scratch. corrscore is a Python package that provides matrix-aware implementations of two established proper scoring rules for this setting -- the energy score and the variogram score -- dispatched across a closed-form tractability spectrum (point, discrete-mixture, and isotropic-Gaussian-mixture forecasts are scored exactly; a general Monte Carlo ensemble falls back to sampling), a geometry-aware variant of the variogram score built from the affine-invariant distance on the correlation manifold, a zero-overlap-by-construction walk-forward backtest harness, and a bundled significance-testing suite (circular block bootstrap, the Diebold-Mariano test, and the Model Confidence Set). We describe the package's design, its point of departure from the existing scoringRules and properscoring packages, and walk through a complete worked example.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01648",
  "author_total": 1,
  "authors": [
    {
      "name": "Vinh Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3cv6h25k53rrr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01648",
  "fulltext_url": null,
  "id": "3cv6h25k53rrr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01648",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.MS"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2003|Financial Risk and Volatility Modeling"
    ]
  ],
  "openalex": "W7207759943",
  "pdf_url": "https://arxiv.org/pdf/2609.01648",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3cv6h25k53rrr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:26Z",
      "native_id": "2609.01648",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01648"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Matrix-Aware Proper Scoring Rules and Significance Testing for Correlation and Covariance Forecasts in Python",
  "title_text": "Matrix-Aware Proper Scoring Rules and Significance Testing for Correlation and Covariance Forecasts in Python",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:26Z",
  "url": "https://research.draconitic.com/paper/3cv6h25k53rrr",
  "venue": "arXiv",
  "versions": []
}