Economics at your fingertips  

Moment based regression algorithms for drift and volatility estimation in continuous-time Markov switching models

Robert J. Elliott, Vikram Krishnamurthy and Jörn Sass

Econometrics Journal, 2008, vol. 11, issue 2, 244-270

Abstract: We consider a continuous time Markov switching model (MSM) which is widely used in mathematical finance. The aim is to estimate the parameters given observations in discrete time. Since there is no finite dimensional filter for estimating the underlying state of the MSM, it is not possible to compute numerically the maximum likelihood parameter estimate via the well known expectation maximization (EM) algorithm. Therefore in this paper, we propose a method of moments based parameter estimator. The moments of the observed process are computed explicitly as a function of the time discretization interval of the discrete time observation process. We then propose two algorithms for parameter estimation of the MSM. The first algorithm is based on a least-squares fit to the exact moments over different time lags, while the second algorithm is based on estimating the coefficients of the expansion (with respect to time) of the moments. Extensive numerical results comparing the algorithm with the EM algorithm for the discretized model are presented. Copyright © 2008 The Authors. Journal compilation © Royal Economic Society 2008

Date: 2008
References: Add references at CitEc
Citations View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link) link to full text (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:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().

Page updated 2017-09-29
Handle: RePEc:ect:emjrnl:v:11:y:2008:i:2:p:244-270