A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Building a dynamic correlation network for fat-tailed financial asset returns
2016
Applied Network Science
In this paper, a novel approach to building a dynamic correlation network of highly volatile financial asset returns is presented. Our method avoids the spurious correlation problem when estimating the dynamic correlation matrix of financial asset returns by using a filtering approach. A multivariate volatility model, DCC-GARCH, is employed to filter the fat-tailed returns. The method is proven to be more reliable for detecting dynamic changes in the correlation matrix compared with the widely
doi:10.1007/s41109-016-0008-x
pmid:30533499
pmcid:PMC6245155
fatcat:syswdvuxffhe5hbr77mjqoojn4