Abstract
The time-varying correlations between multivariate financial time series have been intensively studied. For example DCC and Block-DCC models have been proposed. In this paper, we present a novel Clustered DCC model which extends the previous models by incorporating clustering techniques. Instead of using the same parameters for all time series, a cluster structure is produced based on the autocorrelations of standardized residuals, in which clustered entries sharing the same dynamics. We compare and investigate different clustering methods using synthetic data. To verify the effectiveness of the whole proposed model, we conduct experiments on a set of Hong Kong stock daily returns, and the results outperform the original DCC GARCH model as well as Block-DCC model.
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Zhou, T., Chan, L. (2008). Clustered Dynamic Conditional Correlation Multivariate GARCH Model. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_20
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DOI: https://doi.org/10.1007/978-3-540-85836-2_20
Publisher Name: Springer, Berlin, Heidelberg
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