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Conditional expectile-based value-at-risk: Semiparametric estimation and inference — arXiv:2611.02716

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02716
License (as archived): CC0-1.0
Archived: 2026-11-03T19:07:10.959Z
SHA-256 of the archived text: 7dc92141c5e9b3f960fca697153165ab3def1d97563c63952867e4f7ff3c1aa0
Stable URL of this page: https://metraipolis.com/documento/dc_613112e2c450

How to cite

Conditional expectile-based value-at-risk: Semiparametric estimation and inference — arXiv:2611.02716 — arXiv (en), https://arxiv.org/abs/2611.02716 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_613112e2c450 (Metraipolis archive dc_613112e2c450, sha256 7dc92141c5e9b3f960fca697153165ab3def1d97563c63952867e4f7ff3c1aa0)

BibTeX

@misc{dc613112e2c450,
  title = {Conditional expectile-based value-at-risk: Semiparametric estimation and inference — arXiv:2611.02716},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02716},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_613112e2c450; sha256 7dc92141c5e9b3f960fca697153165ab3def1d97563c63952867e4f7ff3c1aa0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02716. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conditional expectile-based value-at-risk (EVaR) has attracted increasing attention in financial risk management owing to its desirable theoretical properties. However, existing estimation methods for conditional EVaR either rely on potentially misspecified parametric models or suffer from the curse of dimensionality in nonparametric approaches. To this end, we study semiparametric estimation and inference for conditional EVaR in nonlinear time series models with unspecified error distribution. Specifically, we propose a two-step conditional EVaR estimator that achieves the parametric convergence rate. Furthermore, we derive a functional central limit theorem for conditional EVaR process, and then extend to investigate estimating both conditional expectile-based expected shortfall and conditional distribution. A residual bootstrap procedure is proposed for non-expert practitioners to conduct statistical inference. The asymptotic results and their applications are illustrated by numerical studies and an empirical application to S\&P 500 returns.

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/2611.02716 — 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 2026-11-03T18:25:07.861Z dalla pagina https://arxiv.org/list/econ.EM/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).