--- 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 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} } ```