Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall
2021
In this paper new semiparametric GARCH models with long memory are introduced.
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.
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