Modeling Risk Premia in Forward Foreign Exchange Rates as Unobserved Components: The Model Identification Problem

Aziz Chouikh and Abdelwahed Trabelsi

International Journal of Financial Research, 2014, vol. 5, issue 3, 119-135

Abstract: Is an autoregressive moving average model for the unobserved forward risk premium component always identifiable? Is the signal extraction-based approach always feasible? In this paper, we point out a theoretical framework to shed the light on the statistical problem of model identification. We find out that whenever a model for the unobservable forward risk premium is unidentifiable, we identify a new class of functions that we call: the noise generating functions (Hereafter NGF). These functions circumvent the model identification problem and help us make insights on the noise variances. We demonstrate that a model for the risk premium in the forward exchange rate is not always identifiable and the signal extraction methodology is not always feasible. In addition, our theoretical statements are applied to the empirically evidenced models within the related literature.

Keywords: autoregressive moving average; forward risk premium; signal extraction; noise; forward exchange rate (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciedu.ca/journal/index.php/ijfr/article/view/5017/2947 (application/pdf)
http://www.sciedu.ca/journal/index.php/ijfr/article/view/5017 (text/html)

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:jfr:ijfr11:v:5:y:2014:i:3:p:119-135

Access Statistics for this article

International Journal of Financial Research is currently edited by Gina Perry

More articles in International Journal of Financial Research from International Journal of Financial Research, Sciedu Press
Bibliographic data for series maintained by Gina Perry ().