Bayesian Inference for Mixed Gaussian GARCH-Type Model by Hamiltonian Monte Carlo Algorithm

Rubing Liang (), Binbin Qin () and Qiang Xia ()
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Rubing Liang: South China Agricultural University
Binbin Qin: South China Agricultural University
Qiang Xia: South China Agricultural University

Computational Economics, 2024, vol. 63, issue 1, No 8, 193-220

Abstract: Abstract MCMC algorithm is widely used in parameters’ estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well.

Keywords: Mixed Gaussian; GARCH-type models; HMC algorithm; Forecasting; Baysesian inference (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-022-10337-4

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