Performance of MS-GARCH models : Bayesian MCMC-based estimation

Performance of MS-GARCH models Bayesian MCMC-based estimation

In this chapter, both Maximum likelihood estimation (MLE) and Bayesian MCMC estimation methods are used to test their parameters estimation power while estimating a Markov-Switching generalized autoregressive conditional heteroscedasticity (MS-GARCH) model. The monthly exchange rates of BRICS countr...

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Veröffentlicht in:Handbook of research on emerging theories, models, and applications of financial econometrics
1. Verfasser: Xaba, Lawrence Diteboho (VerfasserIn)
Weitere Verfasser: Moroke, Ntebogang Dinah (VerfasserIn), Metsileng, Lebotsa Daniel (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: 2021
Schlagworte:
Bayesian Markov Chain Monte Carlo (MCMC) > Diebold-Mariano test > Error metrices > Maximum likelihood estimation > Nonlinear GARCH model > Aufsatz im Buch
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