---
_id: '66449'
abstract:
- lang: eng
text: "This paper evaluates the forecasting performance of an expanded class of
(semi-)parametric \r\nGARCH models belonging to the EGARCH family (EGF), including
recently introduced long \r\nand short memory specifications and their semiparametric
extensions. The semiparametric \r\nvariants employ a multiplicative volatility
decomposition into conditional and slowly varying \r\nunconditional components,
where the latter is estimated via a data-driven local polynomial \r\nsmoother
to accommodate non-stationarities commonly observed in financial time series.
Based \r\non the revised Basel Committee framework for market-risk assessment,
all models are capable \r\nof producing rolling one-day-ahead forecasts for Value
at Risk (VaR) and Expected Shortfall \r\n(ES) under a wide range of symmetric
and skewed innovation distributions. Their forecasting \r\naccuracy is examined
using the regulatory traffic light tests for VaR and the recently developed \r\nES-specific
traffic light procedure, complemented by the regulatory loss function. In addition,
\r\nmodel selection incorporates both a recently proposed corrected firm-oriented
loss function that \r\naccounts for opportunity costs and the Weighted Absolute
Deviation (WAD) criterion. The \r\nempirical comparison demonstrates that (semiparametric)
long memory GARCH models - \r\nparticularly those combining fractional dynamics
with nonparametric scale adjustments - can \r\nserve as valuable alternatives
to traditional parametric short memory models, offering more \r\nstable volatility
estimates and improved tail-risk forecasts for practical risk management \r\napplications."
author:
- first_name: Dominik Christian
full_name: Hanke, Dominik Christian
id: '63677'
last_name: Hanke
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
citation:
ama: Hanke DC, Uhde A, Feng Y. Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.;
2026.
apa: Hanke, D. C., Uhde, A., & Feng, Y. (2026). Application of Novel Exponential
(Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements
of Basel III.
bibtex: '@book{Hanke_Uhde_Feng_2026, title={Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III},
author={Hanke, Dominik Christian and Uhde, André and Feng, Yuanhua}, year={2026}
}'
chicago: Hanke, Dominik Christian, André Uhde, and Yuanhua Feng. Application
of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under
Regulatory Requirements of Basel III, 2026.
ieee: D. C. Hanke, A. Uhde, and Y. Feng, Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.
2026.
mla: Hanke, Dominik Christian, et al. Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.
2026.
short: D.C. Hanke, A. Uhde, Y. Feng, Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III,
2026.
date_created: 2026-07-13T09:21:49Z
date_updated: 2026-07-16T09:07:19Z
ddc:
- '330'
department:
- _id: '200'
- _id: '186'
file:
- access_level: open_access
content_type: application/pdf
creator: dhanke
date_created: 2026-07-13T09:21:45Z
date_updated: 2026-07-13T09:21:45Z
file_id: '66450'
file_name: TAF_WP_105_HankeUhdeFeng2026.pdf
file_size: 1830858
relation: main_file
file_date_updated: 2026-07-13T09:21:45Z
has_accepted_license: '1'
jel:
- C22
- C4
- C5
- C6
- B26
keyword:
- semiparametric GARCH extension
- data-driven local polynomial smoother
- long memory
- GARCH models
- Value at Risk
- Expected Shortfall
- traffic light test
- backtesting
- Basel III
- market risk
language:
- iso: eng
oa: '1'
status: public
title: Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH
Models under Regulatory Requirements of Basel III
type: working_paper
user_id: '63677'
year: '2026'
...
---
_id: '66447'
abstract:
- lang: eng
text: "Volatility modeling is utilized across numerous fields including finance,
environmental studies, and \r\nsocial sciences. It is particularly relevant in
scenarios where understanding and predicting conditional \r\nvariability is crucial,
such as when dealing with incremental or time-dependent data. In this paper, novel
\r\nshort and long memory volatility models of the EGARCH family are introduced
and analyzed, which \r\nare closely related to the well-established EGARCH model
proposed by Nelson (1991) but share \r\ndesirable theoretical properties in several
dimensions. Recently developed members of the so-called \r\nEGARCH family, which
introduces a modulus-log transformation proposed by John and Draper (1980) \r\nand
a power transformation for the size and magnitude effect to tackle the problem
with near-zero \r\ninnovations and the asymmetric impact of positive and negative
shocks on the volatility, are discussed. \r\nAfter a theoretical discussion of
the proposed and related volatility models, the practical performance \r\nof the
elaborated volatility models is compared to well-established and traditional GARCH
approaches. \r\nA general QMLE algorithm is proposed to estimate the model parameters.
The practical relevance of the \r\nadvanced models is illustrated through a comparative
study. By applying these volatility models to a \r\nvariety of international stock
index returns, this paper identifies market-specific characteristics as well \r\nas
unique strengths and weaknesses of discussed volatility models. Although the practical
performance \r\nof the recently introduced models is comparable to those obtained
by the traditional EGARCH model, \r\nthey generally outperform traditional non-exponential
volatility models used as benchmarks and thus \r\nprovide a useful alternative
to existing short and long memory volatility models. "
author:
- first_name: Dominik Christian
full_name: Hanke, Dominik Christian
id: '63677'
last_name: Hanke
- 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: Hanke DC, Feng Y, Uhde A. Comparing the Behaviors of Some Original Short
and Long Memory Exponential Volatility Models.; 2026.
apa: Hanke, D. C., Feng, Y., & Uhde, A. (2026). Comparing the behaviors of
some original short and long memory exponential volatility models.
bibtex: '@book{Hanke_Feng_Uhde_2026, title={Comparing the behaviors of some original
short and long memory exponential volatility models}, author={Hanke, Dominik
Christian and Feng, Yuanhua and Uhde, André}, year={2026} }'
chicago: Hanke, Dominik Christian, Yuanhua Feng, and André Uhde. Comparing the
Behaviors of Some Original Short and Long Memory Exponential Volatility Models,
2026.
ieee: D. C. Hanke, Y. Feng, and A. Uhde, Comparing the behaviors of some original
short and long memory exponential volatility models. 2026.
mla: Hanke, Dominik Christian, et al. Comparing the Behaviors of Some Original
Short and Long Memory Exponential Volatility Models. 2026.
short: D.C. Hanke, Y. Feng, A. Uhde, Comparing the Behaviors of Some Original Short
and Long Memory Exponential Volatility Models, 2026.
date_created: 2026-07-13T09:15:09Z
date_updated: 2026-07-16T09:07:24Z
ddc:
- '330'
department:
- _id: '186'
file:
- access_level: open_access
content_type: application/pdf
creator: dhanke
date_created: 2026-07-13T09:14:58Z
date_updated: 2026-07-13T09:14:58Z
file_id: '66448'
file_name: TAF_WP_104_HankeFengUhde2026.pdf
file_size: 1169286
relation: main_file
file_date_updated: 2026-07-13T09:14:58Z
has_accepted_license: '1'
jel:
- C4
- C5
- B23
- B26
- C32
keyword:
- Modulus Log-GARCH
- Modified (FI)EGARCH
- Modulus asymmetric (FI)Log-GARCH
- (FI)EGARCH
- long memory
- modulus-log transformation
- QMLE
- model selection
- implementation in R
language:
- iso: eng
oa: '1'
status: public
title: Comparing the behaviors of some original short and long memory exponential
volatility models
type: working_paper
user_id: '63677'
year: '2026'
...
---
_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'
...