BAYESIAN ESTIMATION OF THE GJR-GARCH (p, q) MODEL WITH STUDENT-T PRIOR DISTRIBUTION

Resa Mae R. Sangco

Abstract


The presence of volatility in many financial time series data is one of the problems that cause the variance to be non-constant. The GJR-GARCH (p, q) is a model that takes into account time-varying volatility, allowing positive and negative shocks to have distinct effects. This study provides the estimates of the GJR-GARCH (p, q) model using the Bayesian approach. Student-t distribution is used as prior error distribution. It derives the posterior distribution of the GJR-GARCH (p, q) model with student-t distribution, specifically the parameters α and β, latent variable ω, and degrees of freedom v.


Keywords


Volatility, GJR-GARCH, Bayesian, student-t, posterior distribution

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References


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