  We extend existing models in the financial literature by introducing a
cluster-derived canonical vine (CDCV) copula model for capturing high
dimensional dependence between financial time series. This model utilises a
simplified market-sector vine copula framework similar to those introduced by
Heinen and Valdesogo (2008) and Brechmann and Czado (2013), which can be
applied by conditioning asset time series on a market-sector hierarchy of
indexes. While this has been shown by the aforementioned authors to control the
excessive parameterisation of vine copulas in high dimensions, their models
have relied on the provision of externally sourced market and sector indexes,
limiting their wider applicability due to the imposition of restrictions on the
number and composition of such sectors. By implementing the CDCV model, we
demonstrate that such reliance on external indexes is redundant as we can
achieve equivalent or improved performance by deriving a hierarchy of indexes
directly from a clustering of the asset time series, thus abstracting the
modelling process from the underlying data.
