Analysing Financial Data Using Polya Trees
نویسندگان
چکیده
We present a new approach to generalised autoregressive conditional het-eroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nancial time series, for example, asymmetricity and excessive kurtosis, whilst maintaining the simple, and highly interpretable, GARCH model.
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