Bayesian Change-point Model for ARCH

  • Nam, Seung-Min (Department, Samsung Fire & Marine Insurance) ;
  • Kim, Ju-Won (Dept. of Statistics, Seoul National University) ;
  • Cho, Sin-Sup (Dept. of Statistics, Seoul National University)
  • Published : 2006.12.31


We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.


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