Proximity-Structured Multivariate Volatility Models
In many multivariate volatility models, the number of parameters increases faster than the
cross-section dimension, hence creating a curse of dimensionality problem. This paper discusses
specification and identification of structured parameterizations based on weight matrices induced
by economic proximity. It is shown that structured specifications can mitigate or even solve
the curse of dimensionality problem. Identification and estimation of structured specifications
are analyzed, rank and order conditions for identification are given and the specification of
weight matrices is discussed. Several structured specifications compare well with alternatives in
modelling conditional covariances of six returns from the New York Stock Exchange.
CAPORIN Massimilano;
PARUOLO Paolo;
2015-01-15
TAYLOR & FRANCIS INC
JRC53383
0747-4938,
http://www.tandfonline.com/doi/abs/10.1080/07474938.2013.807102#.VJFIiyuG8t0,
https://publications.jrc.ec.europa.eu/repository/handle/JRC53383,
10.1080/07474938.2013.807102,
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