| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: Autocallable Notes Pricing (Synthetic) |
| task_categories: |
| - tabular-regression |
| tags: |
| - finance |
| - quantitative-finance |
| - derivatives |
| - structured-products |
| - autocallable |
| - option-pricing |
| - monte-carlo |
| - heston |
| - synthetic |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/*/dataset.csv |
| --- |
| |
| # 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](https://github.com/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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** |
|
|
| ```bash |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
