Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265846 
Year of Publication: 
2022
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2022-070/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We introduce a new, easily scalable model for dynamic conditional correlation matrices based on a recursion of dynamic bivariate partial correlation models. By exploiting the model's recursive structure and the theory of perturbed stochastic recurrence equations, we establish stationarity, ergodicity, and filter invertibility in the multivariate setting using conditions for bivariate slices of the data only. From this, we establish consistency and asymptotic normality of the maximum likelihood estimator for the model's static parameters. The new model outperforms benchmarks like the t-cDCC and the multivariate t-GAS, both in simulations and in an in-sample and out-of-sample asset pricing application to 1980–2021 US stock returns across twelve industries
Subjects: 
Dynamic partial correlations
perturbed stochastic recurrence equations
invertibility
stationarity
JEL: 
C32
C58
Document Type: 
Working Paper

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