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Convolution Skew-$t$ Distributions — arXiv:2610.41171

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41171
License (as archived): CC0-1.0
Archived: 2026-11-02T08:13:41.950Z
SHA-256 of the archived text: 0ca50cd8de05f1dd942dd98feb9641c95e22bd99e3806f70dec3b1acd90ac2ee
Stable URL of this page: https://metraipolis.com/documento/dc_4e68cf09fa90

How to cite

Convolution Skew-$t$ Distributions — arXiv:2610.41171 — arXiv (en), https://arxiv.org/abs/2610.41171 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e68cf09fa90 (Metraipolis archive dc_4e68cf09fa90, sha256 0ca50cd8de05f1dd942dd98feb9641c95e22bd99e3806f70dec3b1acd90ac2ee)

BibTeX

@misc{dc4e68cf09fa90,
  title = {Convolution Skew-$t$ Distributions — arXiv:2610.41171},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41171},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e68cf09fa90; sha256 0ca50cd8de05f1dd942dd98feb9641c95e22bd99e3806f70dec3b1acd90ac2ee},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41171. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We introduce the convolution skew-$t$ (CST) distribution, which combines independent standardized skew-$t$ subvectors through a general loading matrix to accommodate heterogeneous tails, directional asymmetry, and non-elliptical dependence. We establish identification and develop likelihood inference using analytical scores and a one-dimensional reduction of Fisher information. For the finite-variance model, we prove consistency and asymptotic normality of maximum likelihood and characterize the consequences of imposing false symmetry. A raw representation extends the family to all positive tail indices. Simulations assess finite-sample performance, and a realized-volatility application illustrates the empirical value of allowing tail thickness and asymmetry to vary across latent subvectors.

Tema degli esploratori: economia dei servizi IA (elenco arXiv econ.EM, nuovi lavori del giorno).
Categorie arXiv: Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2610.41171 — 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 2026-11-02T07:52:15.875Z dalla pagina https://arxiv.org/list/econ.EM/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).