Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
Songxi Chen,
Liang Peng and
Cindy Yu
MPRA Paper from University Library of Munich, Germany
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.
Keywords: Conditional characteristic function; Diffusion processes; Empirical likelihood; Kernel smoothing; L´evy driven processes (search for similar items in EconPapers)
JEL-codes: C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 G0 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:46273
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