Bayesian estimation of GARCH model by hybrid Monte Carlo

Abstract

The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH parameters correctly. The algorithm is rather general and it can be applied to other models like stochastic volatility models.

The 9th Joint Conference on Information Sciences (JCIS), October 8-11, 2006

References in corpus (3)

• Testing and tuning symplectic integrators for Hybrid Monte Carlo algorithm in lattice QCD
• Choice of Integrator in the Hybrid Monte Carlo Algorithm
• Higher Order Hybrid Monte Carlo at Finite Temperature

Cited by in corpus (5)

• Bayesian estimation of GARCH model with an adaptive proposal density
• An Adaptive Markov Chain Monte Carlo Method for GARCH Model
• Bayesian Inference on QGARCH Model Using the Adaptive Construction Scheme
• Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm
• Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive Construction Scheme