Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall

Sebastian Letmathe, Yuanhua Feng and André Uhde ()
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Sebastian Letmathe: Paderborn University
André Uhde: Paderborn University

No 141, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: 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.

Keywords: Semiparametric; long memory; GARCH models; forecasting; Value at Risk; Expected Shortfall; traffic light test; Basel Committee on Banking Supervision (search for similar items in EconPapers)
JEL-codes: C14 C51 C52 G17 G32 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2021-04
New Economics Papers: this item is included in nep-cba, nep-ets, nep-ore and nep-rmg
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