Factor Based Index Trading
Francesco Corielli () and
Massimiliano Marcellino
No 209, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University
Abstract:
Index tracking requires to build a portfolio of stocks (a replica) whose behavior is as close as possible to that of a given stock index. Typically, much fewer stocks should appear in the replica than in the index, and there should be no low frequency (persistent) components in the tracking error. Unfortunately, the latter property is not satisfied by many commonly used methods for index tracking. These are based on the in-sample minimization of a loss function, but do not take into account the dynamic properties of the index components. Instead, we represent the index components with a dynamic factor model, and develop a procedure that, in a first step, builds a replica that is driven by the same persistent factors as the index. In a second step, it is also possible to refine the replica so that it minimizes a loss function, as in the traditional approach. Both Monte Carlo simulations and an application to the EuroStoxx50 index provide substantial support for our approach.
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://repec.unibocconi.it/igier/igi/wp/2002/209.pdf (application/pdf)
Related works:
Journal Article: Factor based index tracking (2006) 
Working Paper: Factor Based Index Tracking (2002) 
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:igi:igierp:209
Ordering information: This working paper can be ordered from
https://repec.unibocconi.it/igier/igi/
Access Statistics for this paper
More papers in Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University via Rontgen, 1 - 20136 Milano (Italy).
Bibliographic data for series maintained by ().