Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III

working Paper Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III Dominik Christian Hanke author 63677 André Uhde author 36049 Yuanhua Feng author 20760 200 department 186 department This paper evaluates the forecasting performance of an expanded class of (semi-)parametric GARCH models belonging to the EGARCH family (EGF), including recently introduced long and short memory specifications and their semiparametric extensions. The semiparametric variants employ a multiplicative volatility decomposition into conditional and slowly varying unconditional components, where the latter is estimated via a data-driven local polynomial smoother to accommodate non-stationarities commonly observed in financial time series. Based on the revised Basel Committee framework for market-risk assessment, all models are capable of producing rolling one-day-ahead forecasts for Value at Risk (VaR) and Expected Shortfall (ES) under a wide range of symmetric and skewed innovation distributions. Their forecasting accuracy is examined using the regulatory traffic light tests for VaR and the recently developed ES-specific traffic light procedure, complemented by the regulatory loss function. In addition, model selection incorporates both a recently proposed corrected firm-oriented loss function that accounts for opportunity costs and the Weighted Absolute Deviation (WAD) criterion. The empirical comparison demonstrates that (semiparametric) long memory GARCH models - particularly those combining fractional dynamics with nonparametric scale adjustments - can serve as valuable alternatives to traditional parametric short memory models, offering more stable volatility estimates and improved tail-risk forecasts for practical risk management applications. https://ris.uni-paderborn.de/download/66449/66450/TAF_WP_105_HankeUhdeFeng2026.pdf application/pdfno 2026 eng semiparametric GARCH extensiondata-driven local polynomial smootherlong memoryGARCH modelsValue at RiskExpected Shortfalltraffic light testbacktestingBasel IIImarket risk Hanke, D. C., Uhde, A., & Feng, Y. (2026). Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III. D. C. Hanke, A. Uhde, and Y. Feng, Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III. 2026. Hanke, Dominik Christian, André Uhde, and Yuanhua Feng. Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III, 2026. 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. Hanke, Dominik Christian, et al. Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III. 2026. Hanke DC, Uhde A, Feng Y. Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.; 2026. @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} } 664492026-07-13T09:21:49Z2026-07-16T09:07:19Z