Software, indices, and live dashboards that implement the lab's quantitative methods. Everything here is open-access and citable; install commands and DOIs are listed per package.
Student-t GJR-GARCH with exogenous regressors in the variance equation
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.
pip install gjr-garch-x
Dependence-robust inference for heavy-tailed cross-asset event studies
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.
pip install robust-eventstudy
A composite early-warning index for systemic risk at the DeFi–TradFi boundary
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.
Machine-checked proofs for results across the lab's papers
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.
These packages are released open-access as the research that produced them is published. If a tool is useful to your work, or broken in an interesting way, we want to hear about it.