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Fast-Vollib: A Fast Implied Volatility Library for Python with PyTorch, JAX, and CUDA Fused-Kernel Backends

2026/04/29 by Raeid Saqur · 2 voices · 1 citation
Computer Science · Economics, Econometrics and Finance · #acm:65H05 #acm:65Y05 #acm:91G20 #acm:91G60 #cs.MS #msc:65H05 #msc:65Y05 #msc:91G20 #msc:91G60 #q-fin.CP #q-fin.MF #q-fin.PR

paper · pdf

5 pages, 1 figure, 1 table. Software announcement / reference note. Code: https://github.com/raeidsaqur/fast-vollib. Install: pip install fast-vollib

arxiv published 2026/04/29 · arxiv created 2026/06/08 · arxiv updated 2026/06/08

Abstract

We present fast-vollib, an open-source Python library that provides high-performance European option pricing, implied volatility (IV) computation, and Greeks under the Black-76, Black-Scholes, and Black-Scholes-Merton models. The library is designed as a drop-in alternative to the de-facto-standard pyvollib and pyvollibvectorized packages, with pluggable PyTorch and JAX execution backends, a CUDA fused-kernel Triton contribution for batched IV workloads, and a compatibility-first public API. In addition to a vectorized Halley-method IV solver, fast-vollib ships an experimental, fully-vectorized implementation of Jäckel's "Let's Be Rational" (LBR) algorithm with NumPy/Numba, torch.compile, JAX, and Triton single-pass GPU kernels for batched option chains. This note announces the library and describes its public API surface, with source, documentation, and packaging artifacts available at: GitHub (https://github.com/raeidsaqur/fast-vollib), Docs (https://raeidsaqur.github.io/fast-vollib/), PyPI (https://pypi.org/project/fast-vollib/).

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