---
_id: '29317'
abstract:
- lang: eng
  text: In this paper new semiparametric GARCH models with long memory are in- troduced.
    The estimation of the nonparametric scale function is carried out by an adapted
    version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised
    recommendations by the Basel Committee to measure market risk in the banks' trading
    books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH
    models are applied to obtain rolling one-step ahead forecasts for the Value at
    Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard
    regulatory traffic light tests (Basel Committee on Banking Supervision, 1996)
    and a newly introduced traffic light test for the ES are carried out for all models.
    The practical relevance of our proposal is demonstrated by a comparative study.
    Our results indicate that semiparametric long memory GARCH models are an attractive
    alternative to their conventional, parametric counterparts.
author:
- first_name: Sebastian
  full_name: Letmathe, Sebastian
  id: '23991'
  last_name: Letmathe
- first_name: Yuanhua
  full_name: Feng, Yuanhua
  id: '20760'
  last_name: Feng
- first_name: André
  full_name: Uhde, André
  id: '36049'
  last_name: Uhde
  orcid: https://orcid.org/0000-0002-8058-8857
citation:
  ama: Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied
    to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. doi:<a href="https://doi.org/10.21314/JOR.2022.044">10.21314/JOR.2022.044</a>
  apa: Letmathe, S., Feng, Y., &#38; Uhde, A. (n.d.). Semiparametric GARCH models
    with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of
    Risk</i>. <a href="https://doi.org/10.21314/JOR.2022.044">https://doi.org/10.21314/JOR.2022.044</a>
  bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
    memory applied to Value at Risk and Expected Shortfall}, DOI={<a href="https://doi.org/10.21314/JOR.2022.044">10.21314/JOR.2022.044</a>},
    journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde,
    André} }'
  chicago: Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH
    Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal
    of Risk</i>, n.d. <a href="https://doi.org/10.21314/JOR.2022.044">https://doi.org/10.21314/JOR.2022.044</a>.
  ieee: 'S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
    memory applied to Value at Risk and Expected Shortfall,” <i>Journal of Risk</i>,
    doi: <a href="https://doi.org/10.21314/JOR.2022.044">10.21314/JOR.2022.044</a>.'
  mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
    to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, doi:<a href="https://doi.org/10.21314/JOR.2022.044">10.21314/JOR.2022.044</a>.
  short: S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (n.d.).
date_created: 2022-01-13T11:23:02Z
date_updated: 2024-04-17T13:34:54Z
department:
- _id: '186'
- _id: '19'
doi: 10.21314/JOR.2022.044
jel:
- C14
- C51
- C52
- G17
- G32
keyword:
- Semiparametric
- long memory
- GARCH models
- forecasting
- Value at Risk
- Expected Shortfall
- traffic light test
- Basel Committee on Banking Supervision
language:
- iso: eng
publication: Journal of Risk
publication_status: inpress
status: public
title: Semiparametric GARCH models with long memory applied to Value at Risk and Expected
  Shortfall
type: journal_article
user_id: '36049'
year: '2022'
...