Bayesian Analysis of Realized Matrix-Exponential GARCH Models
The paper develops a new realized matrix-exponential GARCH (MEGARCH) model, which uses the information of returns and realized measure of co-volatility matrix simultaneously. The paper also considers an alternative multivariate asymmetric function to develop news impact curves. We consider Bayesian MCMC estimation to allow non-normal posterior distributions. For three US financial assets, we compare the realized MEGARCH models with existing multivariate GARCH class models. The empirical results indicate that the realized MEGARCH models outperform the other models regarding in-sample and out-of-sample performance. The news impact curves based on the posterior densities provide reasonable results.
|Keywords||Multivariate GARCH, Realized Measure, Matrix-Exponential, Bayesian Markov chain Monte Carlo method, Asymmetry.|
|JEL||Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C32)|
|Sponsor||Japan Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, and Australian Academy of Science, the Australian Research Council, National Science Council, Ministry of Science and Technology (MOST), Taiwan|
|Series||Tinbergen Institute Discussion Paper Series|
McAleer, M.J, & Asai, M. (2018). Bayesian Analysis of Realized Matrix-Exponential GARCH Models (No. TI 2018-005/III). Tinbergen Institute Discussion Paper Series. Retrieved from http://hdl.handle.net/1765/112496