@techreport{66449,
  abstract     = {{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.}},
  author       = {{Hanke, Dominik Christian and Uhde, André and Feng, Yuanhua}},
  keywords     = {{semiparametric GARCH extension, data-driven local polynomial smoother, long  memory, GARCH models, Value at Risk, Expected Shortfall, traffic light test, backtesting, Basel  III, market risk}},
  title        = {{{Application of Novel Exponential (Semi-)Parametric Short and Long  Memory GARCH Models under Regulatory Requirements of Basel III}}},
  year         = {{2026}},
}

@article{35992,
  abstract     = {{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. }},
  author       = {{Letmathe, Sebastian and Feng, Yuanhua and Uhde, André}},
  journal      = {{Journal of Risk}},
  keywords     = {{long memory, generalized autoregressive conditional heteroscedasticity (GARCH) models, value-at-risk (VaR), expected shortfall (ES), traffic-light test, backtesting}},
  number       = {{2}},
  title        = {{{Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}}},
  volume       = {{25}},
  year         = {{2022}},
}

@article{29317,
  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.}},
  author       = {{Letmathe, Sebastian and Feng, Yuanhua and Uhde, André}},
  journal      = {{Journal of Risk}},
  keywords     = {{Semiparametric, long memory, GARCH models, forecasting, Value at Risk, Expected Shortfall, traffic light test, Basel Committee on Banking Supervision}},
  title        = {{{Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}}},
  doi          = {{10.21314/JOR.2022.044}},
  year         = {{2022}},
}