Safety First Portfolio Selection, Extreme Value Theory and Long Run Asset Risks

Laurens de Haan, Dennis W. Jansen, Kees Koedijk and Casper G. de Vries
Additional contact information
Laurens de Haan: Erasmus University Rotterdam
Dennis W. Jansen: Texas A&M University, Department of Economics
Kees Koedijk: University of Limburg
Casper G. de Vries: Tinbergen Institute Rotterdam

A chapter in Extreme Value Theory and Applications, 1994, pp 471-487 from Springer

Abstract: Abstract The paper motivates the use of the statistical extreme value theory for the problem of portfolio selection in economics, both theoretically and empirically. It is shown that the conventional safety first criterion developed by Roy can be successfully improved upon by exploiting the fat tail property of asset returns. Extreme value theory is seen to provide a better bound than the Chebyshev bound. In the empirical application we calculate minimum threshold return levels given very low exceedance probabilities for bond and equity investors. A proof of a new quantile estimator is obtained in the appendix. The data cover at least a half-century of returns and allow for evaluation of investment risks in the long run.

Keywords: Mutual Fund; Portfolio Selection; Investment Opportunity; Asset Return; Stock Index (search for similar items in EconPapers)
Date: 1994
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-1-4613-3638-9_29

Ordering information: This item can be ordered from
http://www.springer.com/9781461336389

DOI: 10.1007/978-1-4613-3638-9_29

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().