2014/11/18 by David Walsh-Jones, Daniel Jones, Walsh-Jones, David +3
Agricultural and Biological Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Economics and business #Financial Risk and Volatility Modeling #Horticultural and Viticultural Research #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.1411.4970
openalex publication_date 2014/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We extend existing models in the financial literature by introducing a\ncluster-derived canonical vine (CDCV) copula model for capturing high\ndimensional dependence between financial time series. This model utilises a\nsimplified market-sector vine copula framework similar to those introduced by\nHeinen and Valdesogo (2008) and Brechmann and Czado (2013), which can be\napplied by conditioning asset time series on a market-sector hierarchy of\nindexes. While this has been shown by the aforementioned authors to control the\nexcessive parameterisation of vine copulas in high dimensions, their models\nhave relied on the provision of externally sourced market and sector indexes,\nlimiting their wider applicability due to the imposition of restrictions on the\nnumber and composition of such sectors. By implementing the CDCV model, we\ndemonstrate that such reliance on external indexes is redundant as we can\nachieve equivalent or improved performance by deriving a hierarchy of indexes\ndirectly from a clustering of the asset time series, thus abstracting the\nmodelling process from the underlying data.\n