VegaInstitute/OpenAutocall

Autocallable Notes Pricing (Synthetic)

A fully synthetic dataset of autocallable structured-note scenarios paired with their fair value (PV), computed by Monte Carlo simulation under several stochastic models for the underlying asset dynamics. It is designed for derivative pricing with machine learning: training surrogate pricers, benchmarking tabular regressors, and studying how product terms and market conditions map to price.

All data is generated from first-principles simulation. It contains no real market data, no proprietary quotes, and no scraped content — every row is produced by the open-source pipeline at VegaInstitute/RG-ML-Autocall-Dataset.

TL;DR

• Task: tabular regression — predict PV (fair value of the note) from product terms + market state.
• Underlying models: Heston (stochastic volatility).
• Pricing engine: Monte Carlo, with a reported MC standard error (PV_std).
• Scope: single- and multi-asset baskets (1–4 assets), worst-of / min-basket payoffs, optional memory coupons.

Supported tasks

• Tabular regression (primary): learn a fast surrogate that maps (product terms, spots, correlations, implied-vol surfaces) → PV.
• Pricing acceleration / model distillation: approximate the Monte Carlo pricer with a neural or gradient-boosted model.
• Sensitivity & calibration studies: analyze how PV responds to coupons, barriers, correlations, and the volatility surface.

Underlying models

Model	Dynamics	Notes
Heston	Stochastic volatility	Produces a volatility smile; parameters sampled from ranges

Generation parameters

Defaults used by the reference pipeline (see configs/ in the source repo):

Parameter	Value / range
Trading days per year	252
Tenors (years)	{1, 2, 3}
Fixings per year	{1, 2, 4}
Assets per basket	1–4
Coupon	[0.01, 0.50]
Coupon barrier	[0.80, 1.00]
Autocall barrier	[1.00, 1.30]
Put strike	[0.70, 1.30]
Inter-asset correlation	[-0.80, 0.80]
Risk-free rate	0.0
Spot / notional	1.0
Monte Carlo paths	up to 1,000,000
Basket convention	worst-of / min-basket
MC quality filter	scenarios kept when PV_std ≤ 0.01
Heston: mean-reversion κ	[1.0, 10.0]
Heston: vol-of-vol ν	[0.01, 1.0]
Heston: price/var corr ρ	[-0.95, -0.10]
Heston: long-run var θ	[0.01, 0.20]
Heston: initial var V₀	[0.0001, 0.04]

Each model family is sampled over multiple parameter draws (n_models) and several product draws per model (n_observations), so the total row count depends on the generation settings you run. The published files are produced by re-running the pipeline; regenerate or extend them with the CLI documented in the source repository.

Data structure

The data is wide tabular CSV, one row per priced scenario, with ~1,531 columns in the full multi-asset configuration. Columns fall into the following groups.

Identifiers & metadata

Column	Description
value_date	Valuation (pricing) date as a year-fraction
value_date_memory	Valuation date for the memory-coupon feature
model_idx	Index of the sampled model parameter set
observation_idx	Index of the product draw within a model
frequency	Observation/fixing frequency
use_min_basket	Whether the worst-of / min-basket convention applies

Product terms

Column	Description
tenor	Maturity in years
coupon	Coupon rate
coupon_barrier	Coupon barrier (fraction of spot)
autocall_barrier	Autocall (early-redemption) barrier
is_put	Whether a down-and-in put applies at maturity
has_memory	Whether unpaid coupons accumulate (memory effect)
strike_put	Put strike (fraction of spot)

Market state

Column	Description
rate	Risk-free rate
num_assets	Number of underlying assets (1–4)
spot_asset_{1..4}	Initial spot of each asset
value_date_spot_asset_{1..4}	Spot at valuation date for each asset
corr_asset_i_j	Pairwise correlations between assets

Fixing schedule & implied-volatility surfaces

Column	Description
Date{1..12}	Fixing/observation dates (year-fractions)
Asset{a}_Date{d}_vol_{k}	Implied volatility for asset a, fixing date d, strike index k (1–31)

The Asset{a}_Date{d}_vol_{1..31} block encodes a discretized implied-volatility surface: 31 strikes per asset, per fixing date, for up to 4 assets and 12 dates.

Target

Column	Description
PV	Fair value of the note (regression target)
PV_std	Monte Carlo standard error of PV (uncertainty of the label)

Tip: treat PV as the label and PV_std as a per-row noise estimate. Drop model_idx / observation_idx before training — they are bookkeeping indices, not features. Unused asset slots (when num_assets < 4) are zero-filled.

Usage

from datasets import load_dataset

ds = load_dataset("VegaInstitute/autocallable-notes-pricing", split="train")
print(ds.features)        # columns
print(ds[0]["PV"])        # target for the first scenario

With pandas:

import pandas as pd

df = pd.read_csv("hf://datasets/VegaInstitute/autocallable-notes-pricing/data/heston/dataset.csv")
y = df["PV"]
X = df.drop(columns=["PV", "PV_std", "model_idx", "observation_idx"])

Limitations & biases

• Synthetic, not observed. Prices and volatility surfaces come from model assumptions, not traded quotes; a model trained here learns the simulated pricer, not real-market mispricings.
• Label noise. PV carries Monte Carlo error; use PV_std to weight or filter rows.
• Model coverage. Limited to Heston with the parameter ranges above; out-of-range terms are out of distribution.
• Wide and sparse. The volatility-surface block dominates the column count; for num_assets < 4 many columns are zero-filled.
• Rate = 0. Generated with a zero risk-free rate by default.

Source code & reproduction

Generation pipeline, configs, and CLI: https://github.com/VegaInstitute/RG-ML-Autocall-Dataset

python gen.py --model heston

License

Released under Creative Commons Attribution 4.0 International (CC-BY-4.0). You may share and adapt the data, including commercially, with appropriate attribution.

Citation

@misc{vegainstitute_autocall_synthetic,
  title        = {Autocallable Notes Pricing (Synthetic)},
  author       = {Vega Institute},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/VegaInstitute/autocallable-notes-pricing},
  note         = {Synthetic Monte Carlo dataset for pricing autocallable structured notes}
}