---
_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: '23456'
article_type: original
author:
- first_name: Davide
full_name: Mattiolo, Davide
last_name: Mattiolo
- first_name: Eckhard
full_name: Steffen, Eckhard
id: '15548'
last_name: Steffen
orcid: 0000-0002-9808-7401
citation:
ama: Mattiolo D, Steffen E. Highly edge‐connected regular graphs without large factorizable
subgraphs. Journal of Graph Theory. 2021;99(1):107-116. doi:10.1002/jgt.22729
apa: Mattiolo, D., & Steffen, E. (2021). Highly edge‐connected regular graphs
without large factorizable subgraphs. Journal of Graph Theory, 99(1),
107–116. https://doi.org/10.1002/jgt.22729
bibtex: '@article{Mattiolo_Steffen_2021, title={Highly edge‐connected regular graphs
without large factorizable subgraphs}, volume={99}, DOI={10.1002/jgt.22729},
number={1}, journal={Journal of Graph Theory}, author={Mattiolo, Davide and Steffen,
Eckhard}, year={2021}, pages={107–116} }'
chicago: 'Mattiolo, Davide, and Eckhard Steffen. “Highly Edge‐connected Regular
Graphs without Large Factorizable Subgraphs.” Journal of Graph Theory 99,
no. 1 (2021): 107–16. https://doi.org/10.1002/jgt.22729.'
ieee: 'D. Mattiolo and E. Steffen, “Highly edge‐connected regular graphs without
large factorizable subgraphs,” Journal of Graph Theory, vol. 99, no. 1,
pp. 107–116, 2021, doi: 10.1002/jgt.22729.'
mla: Mattiolo, Davide, and Eckhard Steffen. “Highly Edge‐connected Regular Graphs
without Large Factorizable Subgraphs.” Journal of Graph Theory, vol. 99,
no. 1, 2021, pp. 107–16, doi:10.1002/jgt.22729.
short: D. Mattiolo, E. Steffen, Journal of Graph Theory 99 (2021) 107–116.
date_created: 2021-08-19T12:45:45Z
date_updated: 2023-05-16T10:39:20Z
doi: 10.1002/jgt.22729
external_id:
arxiv:
- '1912.09704'
intvolume: ' 99'
issue: '1'
keyword:
- factorization
- perfect matchings
- regular graphs
- r-graphs
language:
- iso: eng
page: 107-116
publication: Journal of Graph Theory
publication_identifier:
issn:
- 0364-9024
- 1097-0118
publication_status: published
status: public
title: Highly edge‐connected regular graphs without large factorizable subgraphs
type: journal_article
user_id: '15540'
volume: 99
year: '2021'
...
---
_id: '48877'
abstract:
- lang: eng
text: OpenML is an online machine learning platform where researchers can easily
share data, machine learning tasks and experiments as well as organize them online
to work and collaborate more efficiently. In this paper, we present an R package
to interface with the OpenML platform and illustrate its usage in combination
with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5,
2016). We show how the OpenML package allows R users to easily search, download
and upload data sets and machine learning tasks. Furthermore, we also show how
to upload results of experiments, share them with others and download results
from other users. Beyond ensuring reproducibility of results, the OpenML platform
automates much of the drudge work, speeds up research, facilitates collaboration
and increases the users’ visibility online.
author:
- first_name: Giuseppe
full_name: Casalicchio, Giuseppe
last_name: Casalicchio
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Michel
full_name: Lang, Michel
last_name: Lang
- first_name: Dominik
full_name: Kirchhoff, Dominik
last_name: Kirchhoff
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Benjamin
full_name: Hofner, Benjamin
last_name: Hofner
- first_name: Heidi
full_name: Seibold, Heidi
last_name: Seibold
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
citation:
ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to
the Machine Learning Platform OpenML. Computational Statistics. 2019;34(3):977–991.
doi:10.1007/s00180-017-0742-2'
apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner,
B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package
to Connect to the Machine Learning Platform OpenML. Computational Statistics,
34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2'
bibtex: '@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019,
title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML},
volume={34}, DOI={10.1007/s00180-017-0742-2},
number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe
and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal
and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd},
year={2019}, pages={977–991} }'
chicago: 'Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal
Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl.
“OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” Computational
Statistics 34, no. 3 (2019): 977–991. https://doi.org/10.1007/s00180-017-0742-2.'
ieee: 'G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp.
977–991, 2019, doi: 10.1007/s00180-017-0742-2.'
mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine
Learning Platform OpenML.” Computational Statistics, vol. 34, no. 3, 2019,
pp. 977–991, doi:10.1007/s00180-017-0742-2.'
short: G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner,
H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:51:17Z
department:
- _id: '819'
doi: 10.1007/s00180-017-0742-2
intvolume: ' 34'
issue: '3'
keyword:
- Databases
- Machine learning
- R
- Reproducible research
language:
- iso: eng
page: 977–991
publication: Computational Statistics
publication_identifier:
issn:
- 0943-4062
status: public
title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML'
type: journal_article
user_id: '102979'
volume: 34
year: '2019'
...