Advanced Idiosyncratic Risk and Multi-Factor Models – Short Version

19 Pages Posted: 1 Feb 2017

See all articles by Jan Dash

Jan Dash

Fordham University; Bloomberg LP

Mario Bondioli

Bloomberg L.P.

Date Written: January 19, 2017

Abstract

We introduce advanced idiosyncratic risk (“AI-Risk”), a parsimonious correlated residual correction to a predictive stress CAPM-like factor model, aimed to get more accurate stock-stock correlations. We find that AI-Risk can be significant for stock portfolios. Inclusion of AI-Risk gives a more realistic risk assessment, consistent with real-world correlation constraints. We also indicate the generalization of AI-Risk to cross-sectional regression factor models, of interest to PMs. This paper is an abridged version.

Keywords: AI-Risk, correlated residuals, accurate correlations, cross-section regression, factor models

JEL Classification: C1, C14, C22, C63, F65, G1, Y1

Suggested Citation

Dash, Jan and Bondioli, Mario, Advanced Idiosyncratic Risk and Multi-Factor Models – Short Version (January 19, 2017). Available at SSRN: https://ssrn.com/abstract=2909755 or http://dx.doi.org/10.2139/ssrn.2909755

Jan Dash (Contact Author)

Fordham University ( email )

113 W. 60th St
New York, NY 10023
United States

Bloomberg LP ( email )

731 Lexington Ave
New York, NY 10022
United States

Mario Bondioli

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
61
Abstract Views
504
rank
433,250
PlumX Metrics