[{"_id":"66449","language":[{"iso":"eng"}],"user_id":"63677","ddc":["330"],"status":"public","title":"Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III","year":"2026","author":[{"last_name":"Hanke","first_name":"Dominik Christian","full_name":"Hanke, Dominik Christian","id":"63677"},{"full_name":"Uhde, André","last_name":"Uhde","first_name":"André","id":"36049"},{"first_name":"Yuanhua","last_name":"Feng","full_name":"Feng, Yuanhua","id":"20760"}],"jel":["C22","C4","C5","C6","B26"],"date_updated":"2026-07-16T09:07:19Z","has_accepted_license":"1","file":[{"content_type":"application/pdf","file_id":"66450","access_level":"open_access","file_size":1830858,"file_name":"TAF_WP_105_HankeUhdeFeng2026.pdf","date_updated":"2026-07-13T09:21:45Z","relation":"main_file","date_created":"2026-07-13T09:21:45Z","creator":"dhanke"}],"date_created":"2026-07-13T09:21:49Z","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"],"type":"working_paper","department":[{"_id":"200"},{"_id":"186"}],"oa":"1","file_date_updated":"2026-07-13T09:21:45Z","citation":{"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} }","ama":"Hanke DC, Uhde A, Feng Y. <i>Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III</i>.; 2026.","mla":"Hanke, Dominik Christian, et al. <i>Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III</i>. 2026.","chicago":"Hanke, Dominik Christian, André Uhde, and Yuanhua Feng. <i>Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III</i>, 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.","ieee":"D. C. Hanke, A. Uhde, and Y. Feng, <i>Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III</i>. 2026.","apa":"Hanke, D. C., Uhde, A., &#38; Feng, Y. (2026). <i>Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III</i>."},"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."}]},{"title":"Comparing the behaviors of some original short  and long memory exponential volatility models","year":"2026","status":"public","author":[{"id":"63677","last_name":"Hanke","first_name":"Dominik Christian","full_name":"Hanke, Dominik Christian"},{"id":"20760","first_name":"Yuanhua","last_name":"Feng","full_name":"Feng, Yuanhua"},{"id":"36049","first_name":"André","last_name":"Uhde","full_name":"Uhde, André"}],"jel":["C4","C5","B23","B26","C32"],"date_updated":"2026-07-16T09:07:24Z","has_accepted_license":"1","_id":"66447","language":[{"iso":"eng"}],"ddc":["330"],"user_id":"63677","file_date_updated":"2026-07-13T09:14:58Z","citation":{"mla":"Hanke, Dominik Christian, et al. <i>Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models</i>. 2026.","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.","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>.","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."},"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. "}],"file":[{"file_name":"TAF_WP_104_HankeFengUhde2026.pdf","access_level":"open_access","file_size":1169286,"relation":"main_file","date_updated":"2026-07-13T09:14:58Z","file_id":"66448","content_type":"application/pdf","creator":"dhanke","date_created":"2026-07-13T09:14:58Z"}],"date_created":"2026-07-13T09:15:09Z","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","oa":"1","department":[{"_id":"186"}]},{"volume":25,"user_id":"36049","_id":"35992","language":[{"iso":"eng"}],"intvolume":"        25","article_type":"original","date_updated":"2023-11-17T10:26:36Z","publication_status":"inpress","author":[{"full_name":"Letmathe, Sebastian","last_name":"Letmathe","first_name":"Sebastian","id":"23991"},{"id":"20760","last_name":"Feng","first_name":"Yuanhua","full_name":"Feng, Yuanhua"},{"id":"36049","last_name":"Uhde","first_name":"André","full_name":"Uhde, André"}],"year":"2022","title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","status":"public","department":[{"_id":"186"},{"_id":"188"}],"type":"journal_article","keyword":["long memory","generalized autoregressive conditional heteroscedasticity (GARCH) models","value-at-risk (VaR)","expected shortfall (ES)","traffic-light test","backtesting"],"date_created":"2023-01-11T10:50:27Z","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. "}],"citation":{"chicago":"Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i> 25, no. 2 (n.d.).","short":"S. Letmathe, Y. Feng, A. Uhde, Journal of Risk 25 (n.d.).","apa":"Letmathe, S., Feng, Y., &#38; Uhde, A. (n.d.). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>, <i>25</i>(2).","ieee":"S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall,” <i>Journal of Risk</i>, vol. 25, no. 2.","ama":"Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. 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} }","mla":"Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, vol. 25, no. 2."},"issue":"2","publication":"Journal of Risk"},{"date_updated":"2024-04-17T13:34:54Z","publication_status":"inpress","title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","year":"2022","status":"public","jel":["C14","C51","C52","G17","G32"],"author":[{"id":"23991","first_name":"Sebastian","last_name":"Letmathe","full_name":"Letmathe, Sebastian"},{"full_name":"Feng, Yuanhua","last_name":"Feng","first_name":"Yuanhua","id":"20760"},{"last_name":"Uhde","orcid":"https://orcid.org/0000-0002-8058-8857","first_name":"André","full_name":"Uhde, André","id":"36049"}],"doi":"10.21314/JOR.2022.044","user_id":"36049","language":[{"iso":"eng"}],"_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."}],"publication":"Journal of Risk","citation":{"short":"S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (n.d.).","chicago":"Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, n.d. <a href=\"https://doi.org/10.21314/JOR.2022.044\">https://doi.org/10.21314/JOR.2022.044</a>.","apa":"Letmathe, S., Feng, Y., &#38; Uhde, A. (n.d.). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. <a href=\"https://doi.org/10.21314/JOR.2022.044\">https://doi.org/10.21314/JOR.2022.044</a>","ieee":"S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall,” <i>Journal of Risk</i>, doi: <a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>.","ama":"Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. doi:<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>","bibtex":"@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}, DOI={<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>}, journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde, André} }","mla":"Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, doi:<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>."},"keyword":["Semiparametric","long memory","GARCH models","forecasting","Value at Risk","Expected Shortfall","traffic light test","Basel Committee on Banking Supervision"],"type":"journal_article","department":[{"_id":"186"},{"_id":"19"}],"date_created":"2022-01-13T11:23:02Z"}]