---
_id: '35992'
abstract:
- lang: eng
text: 'In this paper new semiparametric generalized autoregressive conditional heteroscedasticity
(GARCH) models with long memory are introduced. A multiplicative decomposition
of the volatility into a conditional component and an unconditional component
is assumed. The estimation of the latter is carried out by means of a data-driven
local polynomial smoother. According to the revised recommendations by the Basel
Committee on Banking Supervision to measure market risk in the banks’ trading
books, these new semiparametric GARCH models are applied to obtain rolling one-step
ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk
assets. Standard regulatory traffic-light tests and a newly introduced traffic-light
test for the ES are carried out for all models. In addition, model performance
is assessed via a recently introduced model selection criterion. The practical
relevance of our proposal is demonstrated by a comparative study. Our results
indicate that semiparametric long-memory GARCH models are a meaningful substitute
for their conventional, parametric counterparts. '
article_type: original
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
citation:
ama: Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied
to Value at Risk and Expected Shortfall. Journal of Risk. 25(2).
apa: Letmathe, S., Feng, Y., & Uhde, A. (n.d.). Semiparametric GARCH models
with long memory applied to Value at Risk and Expected Shortfall. Journal of
Risk, 25(2).
bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall}, volume={25}, number={2},
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.” Journal
of Risk 25, no. 2 (n.d.).
ieee: S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall,” Journal of Risk,
vol. 25, no. 2.
mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
to Value at Risk and Expected Shortfall.” Journal of Risk, vol. 25, no.
2.
short: S. Letmathe, Y. Feng, A. Uhde, Journal of Risk 25 (n.d.).
date_created: 2023-01-11T10:50:27Z
date_updated: 2023-11-17T10:26:36Z
department:
- _id: '186'
- _id: '188'
intvolume: ' 25'
issue: '2'
keyword:
- long memory
- generalized autoregressive conditional heteroscedasticity (GARCH) models
- value-at-risk (VaR)
- expected shortfall (ES)
- traffic-light test
- backtesting
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'
volume: 25
year: '2022'
...
---
_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. Journal of Risk. doi:10.21314/JOR.2022.044
apa: Letmathe, S., Feng, Y., & Uhde, A. (n.d.). Semiparametric GARCH models
with long memory applied to Value at Risk and Expected Shortfall. Journal of
Risk. https://doi.org/10.21314/JOR.2022.044
bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall}, DOI={10.21314/JOR.2022.044},
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.” Journal
of Risk, n.d. https://doi.org/10.21314/JOR.2022.044.
ieee: 'S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall,” Journal of Risk,
doi: 10.21314/JOR.2022.044.'
mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
to Value at Risk and Expected Shortfall.” Journal of Risk, doi:10.21314/JOR.2022.044.
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'
...
---
_id: '4667'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Sebastian
full_name: Letmathe, Sebastian
id: '23991'
last_name: Letmathe
citation:
ama: Feng Y, Letmathe S. The Non-Gaussian ESEMIFAR Model. 2018:7.
apa: Feng, Y., & Letmathe, S. (2018). The Non-Gaussian ESEMIFAR Model. Presented
at the European Conference on Data Analysis, Paderborn, Germany.
bibtex: '@article{Feng_Letmathe_2018, series={Book of Abstracts}, title={The Non-Gaussian
ESEMIFAR Model}, author={Feng, Yuanhua and Letmathe, Sebastian}, year={2018},
pages={7}, collection={Book of Abstracts} }'
chicago: Feng, Yuanhua, and Sebastian Letmathe. “The Non-Gaussian ESEMIFAR Model.”
Book of Abstracts, 2018.
ieee: Y. Feng and S. Letmathe, “The Non-Gaussian ESEMIFAR Model.” p. 7, 2018.
mla: Feng, Yuanhua, and Sebastian Letmathe. The Non-Gaussian ESEMIFAR Model.
2018, p. 7.
short: Y. Feng, S. Letmathe, (2018) 7.
conference:
end_date: 6.7.2018
location: Paderborn, Germany
name: European Conference on Data Analysis
start_date: 4.7.2018
date_created: 2018-10-11T12:26:05Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
language:
- iso: eng
page: '7'
series_title: Book of Abstracts
status: public
title: The Non-Gaussian ESEMIFAR Model
type: conference
user_id: '10075'
year: '2018'
...