Gamma-Driven Markov Processes and Extensions with Application to Realized Volatility

Fernanda G. B. Mendes, Wagner Barreto-Souza and Sokol Ndreca

Journal of Business & Economic Statistics, 2025, vol. 43, issue 1, 14-26

Abstract: We propose a novel class of Markov processes for dealing with continuous positive time series data, which is constructed based on a latent gamma effect and named gamma-driven (GD) models. The GD processes possess desirable properties and features: (i) it can produce any desirable invariant distribution with support on R+, (ii) it is time-reversible, and (iii) it has the transition density function given in an explicit form. Estimation of parameters is performed through the maximum likelihood method combined with a Gauss Laguerre quadrature to approximate the likelihood function. The evaluation of the estimators and also confidence intervals of parameters are explored via Monte Carlo simulation studies. Two generalizations of the GD processes are also proposed to handle nonstationary and long-memory time series. We apply the proposed methodologies to analyze the daily realized volatility of the FTSE 100 equity index.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2024.2321375 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:43:y:2025:i:1:p:14-26

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2024.2321375

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().