Modeling Cross Correlation Across Major Financial Markets: A Threshold Approach
34 Pages Posted: 10 Feb 2016
Date Written: February 8, 2016
We propose the threshold conditional correlation (TCC) model that allows for regime changes in the correlations between financial assets. According to this methodolody the dynamics of the correlations change from one state (or regime) to another as a function of observable transition variable(s). The TCC is similar in spirit to a smooth transition conditional correlation but with the appealing feature that it is easier to estimate, even in a high dimensional setting. In particular, estimation of the parameters of the TCC involves a grid search-QMLE method in which the correlation matrix is positive definite by construction. Furthermore, Monte Carlo experiments show the proposed model generally does not suffer from a serious bias even for very large cross-sections (N=100). The methodology is illustrated by evaluating the behaviour of international equities and government bonds, first separately and then jointly. We further generalize our approach by allowing for different parts in the correlation matrix to have their own transition mechanism, while at the same time guaranteeing that the resulting correlation matrix is positive definite. Finally, we evaluate the out-of-sample economic performance of the TCC model against the popular dynamic conditional correlation (DCC) model by using the Engle-Colacito (2006) test and in terms of density forecasts. The results show that threshold model with four regimes outperforms the DCC, mainly in the recent global financial crisis a period with significant shifts in the level of correlations.
Keywords: Threshold Conditional Correlations, Correlation Targeting, Transition Variables, International Equity and Bond Correlations, Density Forecasts
JEL Classification: C14, C58, G10
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