Translator Disclaimer
February 2013 Parameter estimation and model testing for Markov processes via conditional characteristic functions
Song X. Chen, Liang Peng, Cindy L. Yu
Bernoulli 19(1): 228-251 (February 2013). DOI: 10.3150/11-BEJ400

Abstract

Markov processes are used in a wide range of disciplines, including finance. The transition densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available, especially for Lévy-driven processes. We propose an empirical likelihood approach, for both parameter estimation and model specification testing, based on the conditional characteristic function for processes with either continuous or discontinuous sample paths. Theoretical properties of the empirical likelihood estimator for parameters and a smoothed empirical likelihood ratio test for a parametric specification of the process are provided. Simulations and empirical case studies are carried out to confirm the effectiveness of the proposed estimator and test.

Citation

Download Citation

Song X. Chen. Liang Peng. Cindy L. Yu. "Parameter estimation and model testing for Markov processes via conditional characteristic functions." Bernoulli 19 (1) 228 - 251, February 2013. https://doi.org/10.3150/11-BEJ400

Information

Published: February 2013
First available in Project Euclid: 18 January 2013

zbMATH: 1259.62069
MathSciNet: MR3019493
Digital Object Identifier: 10.3150/11-BEJ400

Rights: Copyright © 2013 Bernoulli Society for Mathematical Statistics and Probability

JOURNAL ARTICLE
24 PAGES


SHARE
Vol.19 • No. 1 • February 2013
Back to Top