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How do fitted conditional variances compare with known synthetic GARCH trajectories at a fixed sample size?

Generate the recurrence independently with recorded innovations, freeze estimation settings, and compare parameter recovery and conditional-variance error across all prespecified seeds.

What you could produce

• A pinned, isolated reproducer with generated inputs and a concise result table
• A separately controlled check report with fixed tolerances and disclosed limitations

Before you use it

• A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.

Limits to keep in view

• No project source, package build hook, test, example, or submitted research command has been executed.
• The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.

Source and permission context

Preserve the upstream project name, version, repository link, applicable notices, and contributor attribution when preparing an artifact for reuse.

License evidence recorded. Review the scope and upstream conditions before reuse.

Still unresolved

• Bundled datasets, examples, submodules, vendored code, and dependency licenses have not been audited; the observed top-level license does not clear all of them.
• README and documentation rights were not independently resolved from the main code license; this catalog only links and describes.