An algorithm for nonparametric GARCH modelling

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Tail index estimation for heavy‐tailed models: accommodation of bias in weighted log‐excesses (2007, methodology): “…model, like a parametric or non-parametric generalized autoregressive conditional heteroscedasticity model, to financial data has been done by several researchers (see for instance Bühlmann and McNeil (2002), and references therein) and would also be a possible way to study these sets of data.”
Energy markets volatility modelling using GARCH (2014, methodology): “Volatility is estimated to be slightly less persistent in the model which includes the extended mean equation, indicating that the extended model, (5) and (6), is successful in capturing a larger set of relevant information from the error term than the baseline model, (4) and (6).”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, background): “Bühlmann and McNeil (2002) proposed a nonparametric estimation iterative algorithm, that requires neither the specification of the conditional variance functional form nor that of the conditional density function, and showed that their algorithm gives more precise estimates of the volatility in the…”