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Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models — arXiv:2608.25588

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.25588
License (as archived): CC0-1.0
Archived: 2026-10-18T02:27:58.643Z
SHA-256 of the archived text: 35dafdc37d158d274d178f23370c064280d719798ab59000ec98b66265ac95ac
Stable URL of this page: https://metraipolis.com/documento/dc_bb20dbfa4ae9

How to cite

Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models — arXiv:2608.25588 — arXiv (en), https://arxiv.org/abs/2608.25588 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb20dbfa4ae9 (Metraipolis archive dc_bb20dbfa4ae9, sha256 35dafdc37d158d274d178f23370c064280d719798ab59000ec98b66265ac95ac)

BibTeX

@misc{dcbb20dbfa4ae9,
  title = {Learning Volatility Dependence Networks in UK Equity Markets using Penalised Spatiotemporal ARCH Models — arXiv:2608.25588},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.25588},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb20dbfa4ae9; sha256 35dafdc37d158d274d178f23370c064280d719798ab59000ec98b66265ac95ac},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.25588. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Spatiotemporal ARCH models capture temporal volatility persistence and cross-sectional dependence but typically require a predefined spatial weight matrix. This is restrictive in financial markets, where the dependence network is rarely known. We develop a LASSO-penalised quasi-maximum likelihood estimator that jointly learns a sparse weight matrix and estimates temporal dependence and covariate effects. Monte Carlo experiments show that the method recovers the model parameters and underlying network, with accuracy improving as the temporal sample size increases. We apply the method to daily returns from twenty UK-listed firms and compare the learned network with Euclidean-distance, correlation, autoregressive-similarity and sector-based structures. The learned network improves out-of-sample volatility prediction and reveals directional firm-level and cross-sector dependence not captured by the predefined alternatives. The method provides a data-driven framework for learning interpretable conditional-volatility networks.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-27 in stat.AP).
Categorie arXiv: Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2608.25588 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T02:27:21.260Z dalla pagina https://arxiv.org/catchup/stat.AP/2026-08-27?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).