A Bayesian conditional autoregressive geometric process model for range data
S.T.B. Choy and
Cathy W. S. Chen ()
Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3006-3019
Extreme value theories indicate that the range is an efficient estimator of local volatility in financial time series. A geometric process (GP) framework that incorporates the conditional autoregressive range (CARR)-type mean function is presented for range data. The proposed model, called the conditional autoregressive geometric process range (CARGPR) model, allows for flexible trend patterns, threshold effects, leverage effects, and long-memory dynamics in financial time series. For robustness considerations, a log-t distribution is adopted. Model implementation can be easily done using the WinBUGS package. A simulation study shows that model parameters are estimated with high accuracy. In the empirical study on the range data of an Australian stock market index, the CARGPR model outperforms the CARR model in both in-sample estimation and out-of-sample forecast.
Keywords: Geometric process; Range data; CARR model; Bayesian analysis; WinBUGS (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3006-3019
Access Statistics for this article
Computational Statistics & Data Analysis is currently edited by S.P. Azen
More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Nithya Sathishkumar ().