Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml |
| Facets | — |
| Authors | Shengli Chen, Zili Zhang |
| Published | 2019-12-23 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:1912.11059 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
The study adds attention to an LSTM for implied-volatility-surface forecasts and connects those forecasts to author-reported strategy metrics, making incremental out-of-sample value the central question. (abstract:S2, abstract:S5, abstract:S6)
Main author claims¶
- The authors report: An LSTM network augmented with an attention mechanism can effectively forecast implied volatility smile surfaces. (
abstract:S2,abstract:S5) - The authors report: The forget gate of LSTM provides strong generalization and captures the long memory of financial volatility. (
abstract:S3) - The authors report: Trading strategies constructed using the predicted volatility surfaces yield higher returns and Sharpe ratios than strategies that do not use prediction. (
abstract:S5)
Data, method, or discussion scope¶
The evidence comes from an experimental comparison of trading strategies that use predicted versus non-predicted volatility surfaces. The approach combines deep learning (LSTM) with attention mechanisms. (abstract:S2, abstract:S4, abstract:S5)
Main limitations¶
The abstract does not specify the data source, market, time period, or the out-of-sample testing procedure. The risk of overfitting in the deep learning model and the incremental benefit of the attention mechanism are not quantified. (abstract:S4, abstract:S5, abstract:S6, abstract:S7)
Relationships¶
- None recorded.