[{"language":[{"iso":"eng"}],"department":[{"_id":"35"},{"_id":"200"},{"_id":"202"},{"_id":"475"}],"user_id":"186","_id":"60118","status":"public","publication":"Journal of Applied Statistics","type":"journal_article","doi":"10.1080/02664763.2024.2424920","title":"Forecasting economic growth by combining local linear and standard approaches","volume":52,"author":[{"first_name":"Marlon","last_name":"Fritz","full_name":"Fritz, Marlon"},{"first_name":"Sarah","last_name":"Forstinger","full_name":"Forstinger, Sarah"},{"last_name":"Feng","full_name":"Feng, Yuanhua","id":"20760","first_name":"Yuanhua"},{"full_name":"Gries, Thomas","id":"186","last_name":"Gries","first_name":"Thomas"}],"date_created":"2025-06-03T10:33:51Z","publisher":"Informa UK Limited","date_updated":"2025-11-09T09:49:02Z","intvolume":"        52","page":"1342-1360","citation":{"bibtex":"@article{Fritz_Forstinger_Feng_Gries_2024, title={Forecasting economic growth by combining local linear and standard approaches}, volume={52}, DOI={<a href=\"https://doi.org/10.1080/02664763.2024.2424920\">10.1080/02664763.2024.2424920</a>}, number={7}, journal={Journal of Applied Statistics}, publisher={Informa UK Limited}, author={Fritz, Marlon and Forstinger, Sarah and Feng, Yuanhua and Gries, Thomas}, year={2024}, pages={1342–1360} }","mla":"Fritz, Marlon, et al. “Forecasting Economic Growth by Combining Local Linear and Standard Approaches.” <i>Journal of Applied Statistics</i>, vol. 52, no. 7, Informa UK Limited, 2024, pp. 1342–60, doi:<a href=\"https://doi.org/10.1080/02664763.2024.2424920\">10.1080/02664763.2024.2424920</a>.","short":"M. Fritz, S. Forstinger, Y. Feng, T. Gries, Journal of Applied Statistics 52 (2024) 1342–1360.","apa":"Fritz, M., Forstinger, S., Feng, Y., &#38; Gries, T. (2024). Forecasting economic growth by combining local linear and standard approaches. <i>Journal of Applied Statistics</i>, <i>52</i>(7), 1342–1360. <a href=\"https://doi.org/10.1080/02664763.2024.2424920\">https://doi.org/10.1080/02664763.2024.2424920</a>","ama":"Fritz M, Forstinger S, Feng Y, Gries T. Forecasting economic growth by combining local linear and standard approaches. <i>Journal of Applied Statistics</i>. 2024;52(7):1342-1360. doi:<a href=\"https://doi.org/10.1080/02664763.2024.2424920\">10.1080/02664763.2024.2424920</a>","ieee":"M. Fritz, S. Forstinger, Y. Feng, and T. Gries, “Forecasting economic growth by combining local linear and standard approaches,” <i>Journal of Applied Statistics</i>, vol. 52, no. 7, pp. 1342–1360, 2024, doi: <a href=\"https://doi.org/10.1080/02664763.2024.2424920\">10.1080/02664763.2024.2424920</a>.","chicago":"Fritz, Marlon, Sarah Forstinger, Yuanhua Feng, and Thomas Gries. “Forecasting Economic Growth by Combining Local Linear and Standard Approaches.” <i>Journal of Applied Statistics</i> 52, no. 7 (2024): 1342–60. <a href=\"https://doi.org/10.1080/02664763.2024.2424920\">https://doi.org/10.1080/02664763.2024.2424920</a>."},"year":"2024","issue":"7","publication_identifier":{"issn":["0266-4763","1360-0532"]},"publication_status":"published"},{"abstract":[{"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. ","lang":"eng"}],"status":"public","type":"journal_article","publication":"Journal of Risk","article_type":"original","keyword":["long memory","generalized autoregressive conditional heteroscedasticity (GARCH) models","value-at-risk (VaR)","expected shortfall (ES)","traffic-light test","backtesting"],"language":[{"iso":"eng"}],"_id":"35992","user_id":"36049","department":[{"_id":"186"},{"_id":"188"}],"year":"2022","citation":{"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.","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.).","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).","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).","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.","short":"S. Letmathe, Y. Feng, A. Uhde, Journal of Risk 25 (n.d.).","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} }"},"intvolume":"        25","publication_status":"inpress","issue":"2","title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","date_updated":"2023-11-17T10:26:36Z","date_created":"2023-01-11T10:50:27Z","author":[{"full_name":"Letmathe, Sebastian","id":"23991","last_name":"Letmathe","first_name":"Sebastian"},{"first_name":"Yuanhua","last_name":"Feng","full_name":"Feng, Yuanhua","id":"20760"},{"full_name":"Uhde, André","id":"36049","last_name":"Uhde","first_name":"André"}],"volume":25},{"_id":"29317","department":[{"_id":"186"},{"_id":"19"}],"user_id":"36049","keyword":["Semiparametric","long memory","GARCH models","forecasting","Value at Risk","Expected Shortfall","traffic light test","Basel Committee on Banking Supervision"],"language":[{"iso":"eng"}],"publication":"Journal of Risk","type":"journal_article","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."}],"status":"public","date_updated":"2024-04-17T13:34:54Z","date_created":"2022-01-13T11:23:02Z","author":[{"full_name":"Letmathe, Sebastian","id":"23991","last_name":"Letmathe","first_name":"Sebastian"},{"first_name":"Yuanhua","id":"20760","full_name":"Feng, Yuanhua","last_name":"Feng"},{"last_name":"Uhde","orcid":"https://orcid.org/0000-0002-8058-8857","id":"36049","full_name":"Uhde, André","first_name":"André"}],"title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","doi":"10.21314/JOR.2022.044","publication_status":"inpress","year":"2022","jel":["C14","C51","C52","G17","G32"],"citation":{"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>.","short":"S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (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>. <a href=\"https://doi.org/10.21314/JOR.2022.044\">https://doi.org/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>","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>.","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>."}},{"type":"journal_article","publication":"The R Journal","status":"public","user_id":"186","_id":"50025","language":[{"iso":"eng"}],"keyword":["Statistics","Probability and Uncertainty","Numerical Analysis","Statistics and Probability"],"issue":"1","publication_status":"published","publication_identifier":{"issn":["2073-4859"]},"citation":{"chicago":"Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i> 14, no. 1 (2022): 182–95. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>.","ieee":"Y. Feng, T. Gries, S. Letmathe, and D. 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The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>, <i>14</i>(1), 182–195. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>"},"page":"182-195","intvolume":"        14","year":"2022","date_created":"2023-12-21T12:09:53Z","author":[{"first_name":"Yuanhua","last_name":"Feng","id":"20760","full_name":"Feng, Yuanhua"},{"first_name":"Thomas","id":"186","full_name":"Gries, Thomas","last_name":"Gries"},{"last_name":"Letmathe","full_name":"Letmathe, Sebastian","first_name":"Sebastian"},{"first_name":"Dominik","full_name":"Schulz, Dominik","last_name":"Schulz"}],"volume":14,"publisher":"The R Foundation","date_updated":"2025-11-10T09:32:36Z","doi":"10.32614/rj-2022-017","title":"The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series"},{"status":"public","type":"journal_article","publication":"Asian Economic and Financial Review","language":[{"iso":"eng"}],"_id":"16873","user_id":"26589","department":[{"_id":"203"}],"year":"2020","citation":{"mla":"Peitz, Christian, et al. “The Shanghai-Hong Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH.” <i>Asian Economic and Financial Review</i>, vol. 10, no. 4, Asian Economic and Social Society, 2020, pp. 427–38.","short":"C. 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