Paper Info
Title
A note on Bayesian identification of change points in data sequences
Abstract
Recent research in mathematical methods for finance suggests that time series for financial data should be studied with non-stationary models and with structural changes that include both jumps and heteroskedasticity (with jumps in variance). It has been recognized that discriminating between variations caused by the continuous motion of Brownian shocks and the genuine discontinuities in the path of the process constitutes a challenge for existing computational procedures. This issue is addressed here, using the product partition model (PPM), for performing such discrimination and the estimation of process jump parameters. Computational implementation aspects of PPM applied to the identification of change points in data sequences are discussed. In particular, we analyze the use of a Gibbs sampling scheme to compute the estimates and show that there is no significant impact of such use on the quality of the results. The influence of the size of the data sequence on the estimates is also of interest, as well as the efficiency of the PPM to correctly identify atypical observations occurring in close instants of time. Extensive Monte Carlo simulations attest to the effectiveness of the Gibbs sampling implementation. An illustrative financial time series example is also presented.
Year
DOI
Venue
2008
10.1016/j.cor.2006.02.018
Computers & OR
Keywords
DocType
Volume
process jump parameter,time series,change point,data sequence,Gibbs sampling scheme,illustrative financial time series,financial data,Bayesian identification,Brownian shock,computational implementation aspect,Gibbs sampling implementation,computational procedure
Journal
35
Issue
ISSN
Citations 
1
Computers and Operations Research
4
PageRank 
References 
Authors
0.69
2
6
Name
Order
Citations
PageRank
Rosangela H. Loschi1276.75
F. R. B. Cruz211010.86
R. H. C. Takahashi3305.81
P. L. Iglesias440.69
R. B. Arellano-Valle5112.91
J. MacGregor Smith649661.72