{"year":"2022","_id":"29317","jel":["C14","C51","C52","G17","G32"],"language":[{"iso":"eng"}],"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."}],"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} }","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>.","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>.","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>.","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>","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>"},"date_created":"2022-01-13T11:23:02Z","publication":"Journal of Risk","status":"public","author":[{"full_name":"Letmathe, Sebastian","first_name":"Sebastian","last_name":"Letmathe","id":"23991"},{"full_name":"Feng, Yuanhua","first_name":"Yuanhua","last_name":"Feng","id":"20760"},{"id":"36049","first_name":"André","last_name":"Uhde","full_name":"Uhde, André","orcid":"https://orcid.org/0000-0002-8058-8857"}],"title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","date_updated":"2024-04-17T13:34:54Z","type":"journal_article","user_id":"36049","publication_status":"inpress","doi":"10.21314/JOR.2022.044","keyword":["Semiparametric","long memory","GARCH models","forecasting","Value at Risk","Expected Shortfall","traffic light test","Basel Committee on Banking Supervision"],"department":[{"_id":"186"},{"_id":"19"}]}