scivol
scivol fits univariate conditional volatility models and dynamic conditional correlation (DCC) models in Python. Models are built from components (a mean, a volatility recursion and an innovation density) and estimated jointly by exact maximum likelihood, with analytical gradients and Hessians computed in a standalone C++20 library.
from scivol import ARMA, GJRGARCH, SkewT
result = (ARMA(1, 0) + GJRGARCH(1, 1) + SkewT()).fit(returns)
result.summary()
forecast = result.forecast(10)
Daily returns of an equal-weighted portfolio of 1,381 US stocks, 2015–2024, with ±2 conditional standard deviations from a fitted GJR-GARCH model with skewed-t innovations:
Conditional correlations among five of those stocks from a DCC model; the line is their average and the band the middle half of the ten pairwise correlations:
Models. ARMA, ARX and HARX means; GARCH, GJR-GARCH, EGARCH and APARCH volatility; Normal, Student-t, Hansen's skewed t, GED, generalized hyperbolic, NIG, hyperbolic and variance-gamma innovations; DCC with Gaussian or Student-t copulas. Every combination is one model with one likelihood.
Estimation you can check. Each likelihood and its derivatives are tested against an automatic-differentiation reference. A fit is reported as successful only when the first-order conditions hold in the model's parameter domain; otherwise the estimate comes back with a status that says why. Standard errors are withheld where ordinary inference does not apply. Several starting points are tried by default, because volatility likelihoods often have more than one local maximum.
Speed. On 500 liquid US stocks and ten models, a fit took a median 1–6 ms, 4.9 times faster than arch and 53 times faster than rugarch per fit, while missing its own likelihood maximum less often than either; DCC fits were 20–50 times faster than rmgarch. See the benchmarks for the method and its limits.
Installation
pip install scivol
Wheels for CPython 3.10–3.12 on Linux (x86-64), macOS (Apple Silicon) and Windows (x64); elsewhere pip builds from source, which needs a C++20 compiler, CMake and Ninja.
Documentation
Getting started · Models · Estimation · Inference · Forecasting · Diagnostics · Model selection · API · References
The defaults of the automatic model-selection procedures have not yet been calibrated by simulation.
License
MIT.
1 maintainer
Metadata
1 maintainer
Release files for scivol 0.4.0
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