Abstract
This study develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. An alternative multivariate asymmetric function to develop news impact curves is also considered. We consider Bayesian Markov chain Monte Carlo estimation to allow non-normal posterior distributions and illustrate the usefulness of the algorithm with numerical simulations for two assets. We compare the realized MEGARCH models with existing multivariate GARCH class models for three US financial assets. The empirical results indicate that the realized MEGARCH models outperform the other models regarding out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.
| Original language | English |
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| Journal | Computational Economics |
| DOIs | |
| Publication status | Accepted/In press - 23 Nov 2020 |
Bibliographical note
JEL Classifcation C11 · C32Funding Information:
The authors are most grateful to Yoshi Baba for very helpful comments and suggestions. The first author acknowledges the financial support of the Japan Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science (19K01594), and the Australian Academy of Science. The second author is grateful to the Australian Research Council and the Ministry of Science and Technology (MOST), Taiwan.
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.