[{"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. "}],"citation":{"short":"D.C. Hanke, Y. Feng, A. Uhde, Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models, 2026.","chicago":"Hanke, Dominik Christian, Yuanhua Feng, and André Uhde. <i>Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models</i>, 2026.","ieee":"D. C. Hanke, Y. Feng, and A. Uhde, <i>Comparing the behaviors of some original short  and long memory exponential volatility models</i>. 2026.","apa":"Hanke, D. C., Feng, Y., &#38; Uhde, A. (2026). <i>Comparing the behaviors of some original short  and long memory exponential volatility models</i>.","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} }","ama":"Hanke DC, Feng Y, Uhde A. <i>Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models</i>.; 2026.","mla":"Hanke, Dominik Christian, et al. <i>Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models</i>. 2026."},"file_date_updated":"2026-07-13T09:14:58Z","department":[{"_id":"186"}],"oa":"1","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"],"type":"working_paper","date_created":"2026-07-13T09:15:09Z","file":[{"creator":"dhanke","date_created":"2026-07-13T09:14:58Z","file_size":1169286,"access_level":"open_access","file_name":"TAF_WP_104_HankeFengUhde2026.pdf","date_updated":"2026-07-13T09:14:58Z","relation":"main_file","content_type":"application/pdf","file_id":"66448"}],"has_accepted_license":"1","date_updated":"2026-07-16T09:07:24Z","jel":["C4","C5","B23","B26","C32"],"author":[{"full_name":"Hanke, Dominik Christian","first_name":"Dominik Christian","last_name":"Hanke","id":"63677"},{"id":"20760","last_name":"Feng","first_name":"Yuanhua","full_name":"Feng, Yuanhua"},{"id":"36049","last_name":"Uhde","first_name":"André","full_name":"Uhde, André"}],"title":"Comparing the behaviors of some original short  and long memory exponential volatility models","year":"2026","status":"public","user_id":"63677","ddc":["330"],"_id":"66447","language":[{"iso":"eng"}]},{"publication":"Journal of Graph Theory","issue":"1","date_created":"2021-08-19T12:45:45Z","keyword":["factorization","perfect matchings","regular graphs","r-graphs"],"type":"journal_article","author":[{"full_name":"Mattiolo, Davide","last_name":"Mattiolo","first_name":"Davide"},{"id":"15548","last_name":"Steffen","first_name":"Eckhard","orcid":"0000-0002-9808-7401","full_name":"Steffen, Eckhard"}],"publication_identifier":{"issn":["0364-9024","1097-0118"]},"year":"2021","title":"Highly edge‐connected regular graphs without large factorizable subgraphs","article_type":"original","intvolume":"        99","publication_status":"published","date_updated":"2023-05-16T10:39:20Z","language":[{"iso":"eng"}],"doi":"10.1002/jgt.22729","citation":{"bibtex":"@article{Mattiolo_Steffen_2021, title={Highly edge‐connected regular graphs without large factorizable subgraphs}, volume={99}, DOI={<a href=\"https://doi.org/10.1002/jgt.22729\">10.1002/jgt.22729</a>}, number={1}, journal={Journal of Graph Theory}, author={Mattiolo, Davide and Steffen, Eckhard}, year={2021}, pages={107–116} }","ama":"Mattiolo D, Steffen E. Highly edge‐connected regular graphs without large factorizable subgraphs. <i>Journal of Graph Theory</i>. 2021;99(1):107-116. doi:<a href=\"https://doi.org/10.1002/jgt.22729\">10.1002/jgt.22729</a>","mla":"Mattiolo, Davide, and Eckhard Steffen. “Highly Edge‐connected Regular Graphs without Large Factorizable Subgraphs.” <i>Journal of Graph Theory</i>, vol. 99, no. 1, 2021, pp. 107–16, doi:<a href=\"https://doi.org/10.1002/jgt.22729\">10.1002/jgt.22729</a>.","short":"D. Mattiolo, E. Steffen, Journal of Graph Theory 99 (2021) 107–116.","chicago":"Mattiolo, Davide, and Eckhard Steffen. “Highly Edge‐connected Regular Graphs without Large Factorizable Subgraphs.” <i>Journal of Graph Theory</i> 99, no. 1 (2021): 107–16. <a href=\"https://doi.org/10.1002/jgt.22729\">https://doi.org/10.1002/jgt.22729</a>.","ieee":"D. Mattiolo and E. Steffen, “Highly edge‐connected regular graphs without large factorizable subgraphs,” <i>Journal of Graph Theory</i>, vol. 99, no. 1, pp. 107–116, 2021, doi: <a href=\"https://doi.org/10.1002/jgt.22729\">10.1002/jgt.22729</a>.","apa":"Mattiolo, D., &#38; Steffen, E. (2021). Highly edge‐connected regular graphs without large factorizable subgraphs. <i>Journal of Graph Theory</i>, <i>99</i>(1), 107–116. <a href=\"https://doi.org/10.1002/jgt.22729\">https://doi.org/10.1002/jgt.22729</a>"},"external_id":{"arxiv":["1912.09704"]},"status":"public","_id":"23456","page":"107-116","volume":99,"user_id":"15540"},{"status":"public","volume":34,"user_id":"102979","_id":"48877","page":"977–991","citation":{"mla":"Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019, pp. 977–991, doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","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={<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>}, 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} }","ama":"Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991. doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>","ieee":"G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp. 977–991, 2019, doi: <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","apa":"Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>, <i>34</i>(3), 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>","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.","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.” <i>Computational Statistics</i> 34, no. 3 (2019): 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>."},"intvolume":"        34","date_updated":"2023-12-13T10:51:17Z","author":[{"last_name":"Casalicchio","first_name":"Giuseppe","full_name":"Casalicchio, Giuseppe"},{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Lang, Michel","first_name":"Michel","last_name":"Lang"},{"full_name":"Kirchhoff, Dominik","first_name":"Dominik","last_name":"Kirchhoff"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Hofner, Benjamin","last_name":"Hofner","first_name":"Benjamin"},{"full_name":"Seibold, Heidi","last_name":"Seibold","first_name":"Heidi"},{"last_name":"Vanschoren","first_name":"Joaquin","full_name":"Vanschoren, Joaquin"},{"full_name":"Bischl, Bernd","first_name":"Bernd","last_name":"Bischl"}],"publication_identifier":{"issn":["0943-4062"]},"title":"OpenML: An R Package to Connect to the Machine Learning Platform OpenML","year":"2019","doi":"10.1007/s00180-017-0742-2","language":[{"iso":"eng"}],"abstract":[{"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.","lang":"eng"}],"publication":"Computational Statistics","issue":"3","department":[{"_id":"819"}],"keyword":["Databases","Machine learning","R","Reproducible research"],"type":"journal_article","date_created":"2023-11-14T15:58:57Z"}]