spb/tools-spboucher-ai

1{2  "slug": "arch",3  "name": "arch (Python package)",4  "aliases": ["arch", "bashtage/arch"],5  "vendor": { "name": "Kevin Sheppard", "slug": "kevin-sheppard", "website": "https://bashtage.github.io/arch/" },6  "homepage_url": "https://bashtage.github.io/arch/",7  "docs_url": "https://bashtage.github.io/arch/",8  "repo_url": "https://github.com/bashtage/arch",9  "license_url": "https://github.com/bashtage/arch/blob/main/LICENSE.md",10  "product_type": "library",11  "product_status": "active",12  "summary": "Kevin Sheppard's Python package for univariate ARCH/GARCH volatility models, bootstraps, multiple-comparison procedures, unit-root and cointegration tests",13  "description": "arch is a Python library by Kevin Sheppard for financial econometrics, released under the University of Illinois/NCSA license. Its core is univariate volatility modelling: mean models (constant, AR, HAR, exogenous regressors) combined with ARCH, GARCH, GJR-GARCH, EGARCH, HARCH and related variance processes and normal, Student-t, skewed-t or GED innovations, estimated by maximum likelihood with analytic forecasts, simulation and bootstrap forecasting. Alongside it ships a bootstrap module (IID, stationary, circular block and others) with confidence-interval helpers, multiple-comparison procedures such as the reality check, SPA and model confidence set, unit-root tests (ADF, KPSS, Phillips-Perron, DF-GLS, Zivot-Andrews), cointegration tests and estimators (Engle-Granger, Phillips-Ouliaris) and long-run covariance estimation.\n\nVersion 8.0.0 is current at the time of writing. The package integrates with pandas and statsmodels conventions and is widely used for realized-volatility and risk research on daily or intraday returns. It covers univariate models only; multivariate GARCH, stochastic volatility and high-frequency realized-measure estimators are not included.",14  "capabilities": ["ARCH, GARCH, GJR-GARCH, EGARCH and HARCH volatility models", "Mean models with AR, HAR and exogenous terms", "Analytic, simulation and bootstrap forecasts", "Bootstrap methods: IID, stationary, circular block", "Reality check, SPA and model confidence set", "Unit-root tests: ADF, KPSS, PP, DF-GLS, Zivot-Andrews", "Cointegration tests and long-run covariance estimation"],15  "workflows": ["GARCH volatility forecasts for risk measures", "Model confidence set over competing forecasting models", "Stationarity testing before time-series modelling"],16  "advantages": ["Comprehensive univariate volatility toolkit", "Permissive NCSA licence", "Consistent with pandas and statsmodels"],17  "limitations": ["Univariate models only", "No realized-measure or stochastic-volatility estimators", "Single author-maintained project"],18  "prerequisites": ["Python with NumPy, SciPy, pandas and statsmodels"],19  "audiences": ["Empirical finance researchers", "Risk analysts working in Python"],20  "alternatives": ["statsmodels", "linearmodels", "eviews", "r"],21  "comparison_notes": "statsmodels lacks GARCH-family models, which arch supplies; EViews and R's rugarch offer comparable volatility modelling in other environments; linearmodels, by the same author, covers panel and IV estimation.",22  "categories": [{ "slug": "hf-finance-research", "primary": true }, { "slug": "econometrics-causal-inference" }],23  "tags": ["python", "time-series", "econometrics", "volatility", "open-source"],24  "use_cases": ["analyze-intraday-data", "estimate-causal-effects"],25  "operating_systems": ["macos", "linux", "windows"],26  "execution_modes": ["local"],27  "interfaces": ["library"],28  "deployment_models": ["embedded"],29  "integrations": ["pandas", "NumPy", "statsmodels", "SciPy"],30  "data_formats": ["pandas Series/DataFrame", "NumPy arrays"],31  "languages": ["Python", "Cython"],32  "source_model": "open-source",33  "license_spdx": "NCSA",34  "pricing_model": ["free", "open-source"],35  "self_hostable": true,36  "sources": [37    { "url": "https://bashtage.github.io/arch/", "type": "official_site", "title": "arch 8.0.0 — Introduction", "supports": ["name", "summary", "capabilities", "vendor"], "checked_at": "2026-11-02" },38    { "url": "https://github.com/bashtage/arch", "type": "repository", "title": "bashtage/arch", "supports": ["repo_url", "capabilities"], "checked_at": "2026-11-02" },39    { "url": "https://github.com/bashtage/arch/blob/main/LICENSE.md", "type": "license_page", "title": "arch LICENSE.md", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }40  ],41  "confidence": "high",42  "discovery_method": "editorial-research",43  "depth": "standard",44  "editorial_status": "reviewed",45  "visibility": "public",46  "featured": false,47  "author": "batch-E"48}49