From GARCH to Neural Network for Volatility Forecast¶
Bibliographic record. Follow the original-source link for the publication.
| Field | Value |
|---|---|
| Primary domain | Volatility |
| Other domains | — |
| Methods | Financial Ml |
| Facets | — |
| Authors | Pengfei Zhao, Haoren Zhu, Wilfred Siu Hung NG, Dik Lun Lee |
| Published | 2024-01-29 |
| Source | arXiv Quantitative Finance History |
| Identifiers | arxiv:2402.06642 |
| URL | Open original source |
Editorial synthesis¶
Why it matters¶
The item attempts to build an equivalence between GARCH and neural-network formulations and embed it in a single NN architecture, which is relevant for combining model interpretability with NN capacity; however, claims are still abstract-only. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5, abstract:S6, abstract:S7, abstract:S8)
Main author claims¶
- The authors note that econometrics and ML for volatility forecasting have evolved separately and propose establishing an equivalence between GARCH and NN models. (
abstract:S2,abstract:S3,abstract:S4) - They introduce GARCH-NN and integrate GARCH model counterparts as components of an established NN architecture to inject stylized volatility facts. (
abstract:S5,abstract:S6) - They claim the GARCH-LSTM/GARCH-NN approach yields enhanced outcomes compared to using stochastic models and NN models in isolation. (
abstract:S7,abstract:S8)
Data, method, or discussion scope¶
Evidence scope is limited to stated objectives and aggregate experiment outcomes, without information on data frequency, metrics, backtest structure, or significance framework. (abstract:S4, abstract:S5, abstract:S6, abstract:S7, abstract:S8)
Main limitations¶
The abstract does not define the exact improvement criteria or identify conditions under which gains hold, limiting direct methodological selection. (abstract:S6, abstract:S8)
Relationships¶
- None recorded.