[{"ddc":["330"],"user_id":"63677","_id":"66449","language":[{"iso":"eng"}],"date_updated":"2026-07-16T09:07:19Z","has_accepted_license":"1","status":"public","year":"2026","title":"Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III","author":[{"id":"63677","full_name":"Hanke, Dominik Christian","last_name":"Hanke","first_name":"Dominik Christian"},{"id":"36049","full_name":"Uhde, André","last_name":"Uhde","first_name":"André"},{"id":"20760","first_name":"Yuanhua","last_name":"Feng","full_name":"Feng, Yuanhua"}],"jel":["C22","C4","C5","C6","B26"],"type":"working_paper","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"],"oa":"1","department":[{"_id":"200"},{"_id":"186"}],"file":[{"creator":"dhanke","date_created":"2026-07-13T09:21:45Z","file_name":"TAF_WP_105_HankeUhdeFeng2026.pdf","file_size":1830858,"access_level":"open_access","relation":"main_file","date_updated":"2026-07-13T09:21:45Z","file_id":"66450","content_type":"application/pdf"}],"date_created":"2026-07-13T09:21:49Z","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."}],"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.","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.","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.","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>."}},{"has_accepted_license":"1","date_updated":"2026-07-16T09:07:24Z","jel":["C4","C5","B23","B26","C32"],"author":[{"id":"63677","last_name":"Hanke","first_name":"Dominik Christian","full_name":"Hanke, Dominik Christian"},{"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","ddc":["330"],"user_id":"63677","language":[{"iso":"eng"}],"_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. "}],"citation":{"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>.","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.","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.","short":"D.C. Hanke, Y. Feng, A. Uhde, Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models, 2026.","mla":"Hanke, Dominik Christian, et al. <i>Comparing the Behaviors of Some Original Short  and Long Memory Exponential Volatility Models</i>. 2026.","ama":"Hanke DC, Feng Y, Uhde A. <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} }"},"file_date_updated":"2026-07-13T09:14:58Z","oa":"1","department":[{"_id":"186"}],"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"}]}]