tools.json

{
  "_note": "Published catalogue. friction-marl held pending TMLR review (deanonymisation); epistemic-engine internal — both withheld 11 Jul 2026. tools.html is GENERATED: run `node build-papers.js` after editing. `relatedPapers` links a tool to the paper(s) it came out of; research.html renders the tool beside its paper rather than in a separate catalogue. A tool with no relatedPapers falls into the standalone group at the end.",
  "tools": [
    {
      "id": "gjr-garch-x",
      "name": "GJR-GARCH-X",
      "category": "packages",
      "status": "released",
      "kind": "Python package",
      "version": "0.3.0",
      "tagline": "Student-t GJR-GARCH with exogenous regressors in the variance equation",
      "description": "A small, dependency-light Python implementation of the asymmetric GJR-GARCH(1,1,1) volatility model with Student-t innovations and arbitrary exogenous regressors (the X) entering the conditional variance equation — the specification that appears as TARCH-X in the lab's event-study papers. Built for the cryptocurrency event-study pipeline, where infrastructure and regulatory shocks are encoded as exogenous variance covariates and tested for asymmetric volatility response.",
      "install": "pip install gjr-garch-x",
      "language": "Python",
      "pypi": "gjr-garch-x",
      "zenodo": "10.5281/zenodo.17988193",
      "github": "https://github.com/studiofarzulla/gjr-garch-x",
      "dashboard": null,
      "arxiv": null,
      "docs": null,
      "features": [
        "Asymmetric leverage effect via the GJR indicator term",
        "Student-t innovations for heavy-tailed crypto returns",
        "Exogenous regressors in the variance equation (the -X)",
        "Maximum-likelihood estimation, NumPy/SciPy only"
      ],
      "metrics": [],
      "relatedPapers": [
        "market-reaction-asymmetry"
      ]
    },
    {
      "id": "robust-eventstudy",
      "name": "robust-eventstudy",
      "category": "packages",
      "status": "released",
      "kind": "Python package",
      "version": "0.1.0",
      "tagline": "Dependence-robust inference for heavy-tailed cross-asset event studies",
      "description": "Event studies on cross-listed assets violate the independence assumption their standard errors are built on: the same shock hits every asset at once, so abnormal returns are correlated across the cross-section and naive tests reject far too often. This package implements the inference toolkit used in the lab's cryptocurrency event-study work — cluster-robust and copula-based procedures that keep their nominal size when returns are heavy-tailed and cross-sectionally dependent. Extracted from the event-study pipeline so the correction is reusable outside the paper that motivated it.",
      "install": "pip install robust-eventstudy",
      "language": "Python",
      "pypi": "robust-eventstudy",
      "zenodo": "10.5281/zenodo.21316120",
      "github": "https://github.com/studiofarzulla/robust-eventstudy",
      "dashboard": null,
      "arxiv": null,
      "docs": null,
      "features": [
        "Cluster-robust standard errors for correlated abnormal returns",
        "t-copula bootstrap for heavy-tailed cross-sectional dependence",
        "Design-effect diagnostics — how much your effective N really is",
        "Golden-anchor regression tests pinned to the published results"
      ],
      "metrics": [],
      "relatedPapers": [
        "market-reaction-asymmetry"
      ]
    },
    {
      "id": "asri",
      "name": "ASRI — Aggregated Systemic Risk Index",
      "category": "indices",
      "status": "live",
      "kind": "Live index & dashboard",
      "version": null,
      "tagline": "A composite early-warning index for systemic risk at the DeFi–TradFi boundary",
      "description": "ASRI aggregates four weighted sub-indices — Stablecoin Concentration, DeFi Liquidity, Contagion, and Regulatory Opacity — into a single 0–100 systemic-risk score for cryptocurrency markets. Validated against the Terra/Luna (2022-05), Celsius/3AC (2022-06), FTX (2022-11), and SVB (2023-03) crises, it correctly flagged the February 2025 Bybit hack ($1.5B, the largest exchange theft in history) as non-systemic — ASRI did not spike, because no contagion channels were active. The live dashboard tracks the index, its sub-components, and the HMM risk regime in real time.",
      "install": null,
      "language": null,
      "pypi": null,
      "zenodo": null,
      "github": null,
      "dashboard": "https://asri.dissensus.ai",
      "arxiv": "2602.03874",
      "docs": null,
      "features": [
        "Four-channel composite score: Stablecoin · DeFi Liquidity · Contagion · Opacity",
        "Alert bands — Low <30 · Moderate 30–50 · Elevated 50–70 · High ≥70",
        "Three-regime hidden Markov model: Low Risk · Moderate · Crisis",
        "Lead time ≈19d (fixed threshold) / ≈26d (walk-forward) before crisis onset"
      ],
      "metrics": [
        {
          "label": "AUROC (ASRI / D-Y)",
          "value": "0.866 / 0.670"
        },
        {
          "label": "AUPRC (ASRI / D-Y)",
          "value": "0.298 / 0.121"
        },
        {
          "label": "Precision @ Youden",
          "value": "35.2% / 14.9%"
        }
      ],
      "relatedPapers": [
        "asri"
      ]
    },
    {
      "id": "farzulla-proofs",
      "name": "FarzullaProofs — Lean 4 Formalisations",
      "category": "research",
      "status": "released",
      "kind": "Lean 4 proof library",
      "version": null,
      "tagline": "Machine-checked proofs for results across the lab's papers",
      "description": "A Lean 4 + Mathlib library formalising core results from the research programme — the Axiom of Consent friction functional, the Replicator-Optimization Mechanism, and supporting results — with formalisation depth tiered (Bronze/Silver/Gold) and tracked per paper. The library builds clean with zero `sorry` placeholders and no added axioms; each paper module ships a map from manuscript theorem IDs to Lean declarations, so a referee can go from an equation in a PDF to the machine-checked statement in one hop.",
      "install": null,
      "language": "Lean 4",
      "pypi": null,
      "zenodo": null,
      "github": "https://github.com/dissensus-ai/lean-formalizations",
      "dashboard": null,
      "arxiv": null,
      "docs": null,
      "features": [
        "Friction-functional results proved: F = 0 ⇔ σ = 0, positivity, the F ≥ σ/2 lower bound",
        "Per-paper theorem maps linking manuscript IDs to Lean declarations",
        "Explicit assumptions ledger — no silent axioms",
        "CI-checked builds on Lean v4.27.0 + Mathlib"
      ],
      "metrics": [
        {
          "label": "Theorems",
          "value": "246 across 15 paper modules"
        },
        {
          "label": "Incomplete proofs",
          "value": "0 sorry · 0 custom axioms"
        }
      ],
      "relatedPapers": [
        "axiom-of-consent",
        "replicator-optimization-mechanism"
      ]
    }
  ],
  "toolStatuses": {
    "live": "Live",
    "released": "Released",
    "beta": "Beta",
    "wip": "In Development",
    "internal": "Internal"
  },
  "toolCategories": {
    "packages": "Software Packages",
    "indices": "Live Indices & Dashboards",
    "research": "Research Code & Formalisations"
  },
  "categoryOrder": [
    [
      "packages",
      "Open-source libraries implementing the lab's quantitative methods"
    ],
    [
      "indices",
      "Hosted indices and interactive risk dashboards"
    ],
    [
      "research",
      "Experiment codebases and machine-checked formalisations behind the papers, released as the research is published"
    ]
  ]
}