Browsing by Author "Iglesias, P. L."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A note on Bayesian identification of change points in data sequences
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Loschi, R. H.; Cruz, F. R. B.; Takahashi, R. H. C.; Iglesias, P. L.; Arellano Valle, R. B.; Smith, J. MacGregor
    Recent research in mathematical methods for finance suggests that time series for financial data should be studied with nonstationary 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. (C) 2006 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Item
    Full predictivistic modeling of stock market data: Application to change point problems
    (ELSEVIER SCIENCE BV, 2007) Loschi, R. H.; Iglesias, P. L.; Arellano Valle, R. B.; Cruz, F. R. B.
    In change point problems in general we should answer three questions: how many changes are there? Where are they? And, what is the distribution of the data within the blocks? In this paper, we develop a new full predictivistic approach for modeling observations within the same block of observation and consider the product partition model (PPM) for treating the change point problem. The PPM brings more flexibility into the change point problem because it considers the number of changes and the instants when the changes occurred as random variables. A full predictivistic characterization of the model can provide a more tractable way to elicit the prior distribution of the parameters of interest, once prior opinions will be required only about observable quantities. We also present an application to the problem of identifying multiple change points in the mean and variance of a stock market return time series. (c) 2006 Elsevier B.V. All rights reserved.