@techreport{66447,
  abstract     = {{Volatility modeling is utilized across numerous fields including finance, environmental studies, and 
social sciences. It is particularly relevant in scenarios where understanding and predicting conditional 
variability is crucial, such as when dealing with incremental or time-dependent data. In this paper, novel 
short and long memory volatility models of the EGARCH family are introduced and analyzed, which 
are closely related to the well-established EGARCH model proposed by Nelson (1991) but share 
desirable theoretical properties in several dimensions. Recently developed members of the so-called 
EGARCH family, which introduces a modulus-log transformation proposed by John and Draper (1980) 
and a power transformation for the size and magnitude effect to tackle the problem with near-zero 
innovations and the asymmetric impact of positive and negative shocks on the volatility, are discussed. 
After a theoretical discussion of the proposed and related volatility models, the practical performance 
of the elaborated volatility models is compared to well-established and traditional GARCH approaches. 
A general QMLE algorithm is proposed to estimate the model parameters. The practical relevance of the 
advanced models is illustrated through a comparative study. By applying these volatility models to a 
variety of international stock index returns, this paper identifies market-specific characteristics as well 
as unique strengths and weaknesses of discussed volatility models. Although the practical performance 
of the recently introduced models is comparable to those obtained by the traditional EGARCH model, 
they generally outperform traditional non-exponential volatility models used as benchmarks and thus 
provide a useful alternative to existing short and long memory volatility models. }},
  author       = {{Hanke, Dominik Christian and Feng, Yuanhua and Uhde, André}},
  keywords     = {{Modulus Log-GARCH, Modified (FI)EGARCH, Modulus asymmetric (FI)Log-GARCH, (FI)EGARCH, long memory, modulus-log transformation, QMLE, model selection, implementation in  R}},
  title        = {{{Comparing the behaviors of some original short  and long memory exponential volatility models}}},
  year         = {{2026}},
}

@article{23456,
  author       = {{Mattiolo, Davide and Steffen, Eckhard}},
  issn         = {{0364-9024}},
  journal      = {{Journal of Graph Theory}},
  keywords     = {{factorization, perfect matchings, regular graphs, r-graphs}},
  number       = {{1}},
  pages        = {{107--116}},
  title        = {{{Highly edge‐connected regular graphs without large factorizable subgraphs}}},
  doi          = {{10.1002/jgt.22729}},
  volume       = {{99}},
  year         = {{2021}},
}

@article{48877,
  abstract     = {{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       = {{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}},
  issn         = {{0943-4062}},
  journal      = {{Computational Statistics}},
  keywords     = {{Databases, Machine learning, R, Reproducible research}},
  number       = {{3}},
  pages        = {{977–991}},
  title        = {{{OpenML: An R Package to Connect to the Machine Learning Platform OpenML}}},
  doi          = {{10.1007/s00180-017-0742-2}},
  volume       = {{34}},
  year         = {{2019}},
}