Portfolio Optimization Under Credit Risk

Rudi Zagst (), Jan Kehrbaum () and Bernd Schmid ()

Computational Statistics, 2003, vol. 18, issue 3, 317-338

Abstract: Based on the models of Hull & White (1990) for the pricing of non-defaultable bonds and Schmid & Zagst (2000) for the pricing of defaultable bonds we develop a framework for the optimal allocation of assets out of a universe of sovereign bonds with different time to maturity and quality of the issuer. We estimate the model parameters by applying Kaiman filtering methods as described in (Schmid & Kalemanova 2002). Based on these estimates we simulate the prices for a given set of bonds for a future time horizon. For each future time step and for each given portfolio composition these scenarios yield distributions of future cash flows and portfolio values. We show how the portfolio composition can be optimized by maximizing the expected final value or return of the portfolio under given constraints. Copyright Physica-Verlag 2003

Keywords: Portfolio Optimization; Defaultable Bonds (search for similar items in EconPapers)
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1007/BF03354601 (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: https://EconPapers.repec.org/RePEc:spr:compst:v:18:y:2003:i:3:p:317-338

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/BF03354601

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().