Bayesian estimation of GARCH model with an adaptive proposal density

@article{Takaishi2010BayesianEO,
  title={Bayesian estimation of GARCH model with an adaptive proposal density},
  author={Tetsuya Takaishi},
  journal={arXiv: Statistical Finance},
  year={2010},
  pages={635-643}
}
  • T. Takaishi
  • Published 29 December 2010
  • Business
  • arXiv: Statistical Finance
A Bayesian estimation of a GARCH model is performed for US Dollar/Japanese Yen exchange rate by the Metropolis-Hastings algorithm with a proposal density given by the adaptive construction scheme. In the adaptive construction scheme the proposal density is assumed to take a form of a multivariate Student's t-distribution and its parameters are evaluated by using the sampled data and updated adaptively during Markov Chain Monte Carlo simulations. We find that the autocorrelation times between… 

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References

SHOWING 1-10 OF 15 REFERENCES

An Adaptive Markov Chain Monte Carlo Method for GARCH Model

It turns out that autocorrelations between the data generated with the adaptive proposal density are greatly reduced and it is concluded that the adaptive construction method is very efficient and works well for the MCMC simulations of the GARCH model.

Bayesian Estimation of GARCH Model by Hybrid Monte Carlo

This work demonstrates that how the HMC reproduces the GARCH parameters correctly and it can be applied to other models like stochastic volatility models.

Stochastic Volatility: Likelihood Inference And Comparison With Arch Models

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective

Bayesian Inference on GARCH Models Using the Gibbs Sampler

It is shown that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied to each coordinate of the posterior density to perform Bayesian inference on GARCH models.

Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional

Comparison of MCMC Methods for Estimating GARCH Models

This paper found the best method is the tailored approach based on the acceptance-rejection Metropolis-Hastings algorithm for estimating the class of ARCH models.

Quadratic Arch Models

We introduce a new model for time-varying conditional variances as the most general quadratic version possible within the ARCH class. Hence, it encompasses all the existing restricted quadratic

Generalized autoregressive conditional heteroskedasticity

CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH

This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates