{"id":4881,"library":"arch","title":"arch: ARCH for Python","description":"ARCH is a Python library providing tools for financial econometrics, focusing on Autoregressive Conditional Heteroskedasticity (ARCH) models, unit root tests, cointegration analysis, and bootstrapping. It is actively developed with regular releases, with the current version being 8.0.0, requiring Python 3.10 or newer for optimal compatibility and performance.","status":"active","version":"8.0.0","language":"en","source_language":"en","source_url":"https://github.com/bashtage/arch","tags":["econometrics","finance","time-series","volatility","ARCH","GARCH","statistics"],"install":[{"cmd":"pip install arch","lang":"bash","label":"Standard installation"},{"cmd":"pip install 'arch[all]'","lang":"bash","label":"With all optional dependencies for full functionality"}],"dependencies":[{"reason":"Fundamental package for numerical computing in Python, essential for array operations and mathematical functions underlying econometric models.","package":"numpy","optional":false},{"reason":"Provides scientific computing tools, including advanced mathematical algorithms and statistical functions used in ARCH models.","package":"scipy","optional":false},{"reason":"Offers high-performance, easy-to-use data structures and data analysis tools, crucial for handling time-series financial data.","package":"pandas","optional":false},{"reason":"Integrates closely for a wide range of statistical models and tests beyond what `arch` directly implements.","package":"statsmodels","optional":false},{"reason":"Used for plotting and visualizing results from econometric models.","package":"matplotlib","optional":true},{"reason":"Optional dependency to improve performance through C extensions.","package":"cython","optional":true},{"reason":"Optional dependency for just-in-time compilation to further enhance performance, especially if Cython is not used or a compiler is unavailable.","package":"numba"}],"imports":[{"symbol":"arch_model","correct":"from arch import arch_model"},{"symbol":"univariate","correct":"from arch import univariate"},{"symbol":"unitroot","correct":"from arch import unitroot"}],"quickstart":{"code":"import datetime as dt\nimport pandas_datareader.data as web\nfrom arch import arch_model\n\n# Fetch financial data (example: FTSE 100 index)\n# Requires 'pandas_datareader' to be installed (pip install pandas_datareader)\nst = dt.datetime(1990,1,1)\nen = dt.datetime(2014,1,1)\ndata = web.DataReader('^FTSE', 'yahoo', start=st, end=en)\nreturns = 100 * data['Adj Close'].pct_change().dropna()\n\n# Initialize and fit an ARCH model\nam = arch_model(returns, vol='Garch', p=1, o=1, q=1, dist='StudentsT')\nres = am.fit(update_freq=5)\nprint(res.summary())","lang":"python","description":"This example demonstrates how to fetch historical financial data, calculate returns, and then use `arch_model` to specify and fit a GARCH(1,1,1) model with a Student's T distribution. It prints a summary of the fitted model. Note that `pandas_datareader` is used for data fetching and needs to be installed separately."},"warnings":[{"fix":"Upgrade your Python environment to 3.10 or newer. If you encounter build issues, ensure your environment is compatible with Meson or consider `--no-binary` installation if performance is not critical and a compiler is unavailable.","message":"Version 8.0.0 and later require Python 3.10+. Older Python versions are no longer supported. The build system has also moved from setuptools to Meson.","severity":"breaking","affected_versions":"8.0.0+"},{"fix":"Always preprocess your time-series data carefully using libraries like Pandas. Validate data types and structures before feeding them into `arch` models. Install any external data acquisition libraries required for your specific workflow separately.","message":"When using `arch` for financial econometrics, proper handling and preparation of time-series data (e.g., ensuring correct datetime indices, handling missing values, and calculating returns appropriately) is crucial for valid model results. External libraries like `pandas_datareader` (used in quickstart) are not core dependencies and must be installed separately.","severity":"gotcha","affected_versions":"All"},{"fix":"Always use virtual environments (e.g., `venv`, `conda`, `pyenv`) for your Python projects to isolate dependencies. Install all project-specific packages within the activated virtual environment using `pip`.","message":"Mixing `pip` installations with system-managed Python packages (e.g., via `pacman` on Arch Linux) can lead to conflicts, especially during Python version updates. This can break virtual environments and lead to 'module not found' errors.","severity":"gotcha","affected_versions":"All"}],"env_vars":null,"last_verified":"2026-04-12T00:00:00.000Z","next_check":"2026-07-11T00:00:00.000Z"}