Nonlinear Features of Realized FX Volatility
John Maheu and
Thomas McCurdy
The Review of Economics and Statistics, 2002, vol. 84, issue 4, 668-681
Abstract:
This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to estimate ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process under duration-dependent mixing. This process can capture large abrupt changes in the level of volatility, time-varying persistence, and time-varying variance of volatility. The results have implications for forecast precision, hedging, and pricing of derivatives. © 2002 President and Fellows of Harvard College and the Massachusetts Institute of Technology.
Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (57)
Downloads: (external link)
http://www.mitpressjournals.org/doi/pdf/10.1162/003465302760556486 link to full text (application/pdf)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Nonlinear Features of Realized FX Volatility (2001) 
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:tpr:restat:v:84:y:2002:i:4:p:668-681
Ordering information: This journal article can be ordered from
https://mitpressjour ... rnal/?issn=0034-6535
Access Statistics for this article
The Review of Economics and Statistics is currently edited by Pierre Azoulay, Olivier Coibion, Will Dobbie, Raymond Fisman, Benjamin R. Handel, Brian A. Jacob, Kareen Rozen, Xiaoxia Shi, Tavneet Suri and Yi Xu
More articles in The Review of Economics and Statistics from MIT Press
Bibliographic data for series maintained by The MIT Press ().