Abstract
A Bayesian multiple changepoint model for the Markov jump process is formulated as a Markov double chain model in continuous time. Inference for this type of multiple changepoint model is based on a two-block Gibbs sampling scheme. We suggest a continuous-time version of forward-filtering backward-sampling (FFBS) algorithm for sampling the full trajectories of the latent Markov chain via inverse transformation. We also suggest a continuous-time version of Viterbi algorithm for this Markov double chain model, which is viable for obtaining the MAP estimation of the set of locations of changepoints. The model formulation and the continuous-time version of forward-filtering backward-sampling algorithm and Viterbi algorithm can be extended to simultaneously monitor the structural breaks of multiple Markov jump processes, which may have either variable transition rate matrix or identical transition rate matrix. We then perform a numerical study to demonstrate the methods. In numerical examples, we compare the performance of the FFBS algorithm via inverse transformation with the FFBS algorithm via uniformization. The two algorithms performs well in different cases. These models are potentially viable for modelling the credit rating dynamics in credit risk management.
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Appendix
Appendix
Note that \(\{X(t), Y^{(1}(t), Y^{(2}(t)\}\) is a Markov Process. The density of the observable process on the space of sample path can be evaluated as follows. Assume that the process jumps N times in [0, T]. The likelihood can then be obtained from the joint density of the random variables \(\{(Y_1,\Delta t_1), (Y_2,\Delta t_2), \ldots , (Y_n,\Delta t_n)\}\). An expression for the density \(P\{x_{k+1}, Y_{k+1}, \Delta t_k\}\) can be derived from a similar argument in (Ball et al. 1994). Specifically, suppose that at time \(t=0\), the latent Markov chain is in state \(X(0)=i\) and the observable process \(Y^{(1}(0)=u, Y^{(2}(0)=l\). \(\Delta t_1=t\) is the time of the first jump of the observed Markov jump process, e.g. \(Y^{(1}(t)\). The conditional probability of the event \(\{x(t)=j, Y^{(1}(t)=v, Y^{(2}(t)=l, \Delta t_1\in [t, t+dt), v\ne u\}\) given \(\{x(0)=i, Y^{(1}(0)=u, Y^{(2}(0)=l\}\) is of interest. The first case is given by the second term on the right of the above equation. The first term on the right-hand of the above equation is for the case when the latent chain has no jump in [0, T]. We denote the corresponding density by \(f_{ij}^{(u,v;l,l)}(t)\).
where \(\delta _{ij}\) is the Kronecker delta function. The above expression may be explained as follows. The first jump of the observable process, e.g. \(Y^{(1}(t)\), from state u to v at time t may occur while the underlying Markov chain first jumps to some intermediate state \(k\ne j\) at some random time \(s < t\), before it eventually the final state j at time t. The jump of the observed process may also occur while the latent Markov chain remains in \(x(0)=i\) for the case \(j=i\). With a variable substitution, the second term of the right of the equality is given by
By differentiating \(f_{ij}^{(u,v;l,l)}(t)\) with respect to t, we obtain
so,
With initial value \(f^{(u,v;l,l)}(0)=D_{uv}^{(1)}\), we have
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Shaochuan, L. Bayesian multiple changepoints detection for Markov jump processes. Comput Stat 35, 1501–1523 (2020). https://doi.org/10.1007/s00180-020-00956-6
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DOI: https://doi.org/10.1007/s00180-020-00956-6