SSE/EFI Working Paper Series in Economics and Finance
Testing for Volatility Interactions in the Constant Conditional Correlation GARCH Model
() and Timo Teräsvirta
Abstract: In this paper we propose a Lagrange multiplier test for
volatility interactions among markets or assets. The null hypothesis is the
Constant Conditional Correlation GARCH model in which volatility of an
asset is described only through lagged squared innovations and volatility
of its own. The alternative hypothesis is an extension of that model in
which volatility is modelled as a linear combination not only of its own
lagged squared innovations and volatility but also of those in the other
equations while keeping the conditional correlation structure constant.
This configuration enables us to test for volatility transmissions among
variables in the model. Monte Carlo experiments show that the proposed test
has satisfactory finite sample properties. The size distortions become
negligible when the sample size reaches 2500. The test is applied to pairs
of foreign exchange returns and individual stock returns. Results indicate
that there seem to be volatility interactions in the pairs considered, and
that significant interaction effects typically result from the lagged
squared innovations of the other variables.
Keywords: Multivariate GARCH; Volatility interactions; Lagrange multiplier test; Monte Carlo simulation; Conditional correlations; (follow links to similar papers)
JEL-Codes: C12; C32; C51; C52; G19; (follow links to similar papers)
21 pages, January 5, 2007, Revised May 4, 2008
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- Publication Data:
- Published in Econometrics Journal, 2009, pages 147-163.
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