Explicit Black-Scholes Implied Volatility Demo Code

This repository contains demo Python and R code accompanying the paper:

An Explicit Solution to Black-Scholes Implied Volatility
Wolfgang Schadner
SSRN

Scope

This repository contains demonstration implementations of the inverse-Gaussian implied-volatility formula. It is not the fast/native implementation used for the numerical results or speed benchmark reported in the paper. The higher-level scripts are arranged to make repeated runs and cross-checks easier while keeping the numerical method itself close to the paper.

Files

• example_ivol.py - Python demo script for batched accuracy and timing comparisons against py_vollib
• example_ivol.R - R demo script for computing implied volatility from normalized inputs and saving a short summary

Requirements

For Python:

python -m pip install -r requirements.txt

For R:

The R script uses base R functions only and does not require additional packages.

Run

Python:

python example_ivol.py

This script writes iv_accuracy_speed_summary.txt in the working directory.

Useful options:

python example_ivol.py --runs 5 --reps 500
python example_ivol.py --skip-lbr
python example_ivol.py --output custom_summary.txt

R:

Rscript example_ivol.R

This script writes iv_r_summary.txt in the working directory.

You can also choose the R summary output path:

Rscript example_ivol.R custom_r_summary.txt

Notes

This code is provided for research and demonstration purposes only. No warranty is provided.