Abstract

http://ssrn.com/abstract=1457022
 


 



Efficient Bayesian Factor Mimicking: Methodology, Tests and Comparison


Wing Cheung


Independent

Nikhil Mittal


Independent

October 18, 2009


Abstract:     
When investment or hedging views are generated on a factor which is not directly investible, creating a quality factor proxy or mimicking portfolio becomes a basic implementation requirement. For fundamental factors, traditional factor-mimicking techniques include the Fama-French (FF) factor-ranking approach (Fama-French, 1993), and constrained optimisation that controls portfolio exposure to factors. In a seemingly different connection, Cheung (2009B) shows how to construct factor portfolios in the Augmented Black-Litterman (ABL) framework, which makes its intrinsic choice of factor-mimicking technique. In this paper, we test the performance of this technique, along with traditional techniques. Our results show that the ABL factor-mimicking technique is more efficient.

This article features: -
- A brief review of two families of traditional and the new ABL FM techniques;
- A simulation-based testing methodology that isolates the FM quality issue from peripheral risk model and view quality issues, thereby avoiding unnecessary joint tests; and
- Numerical comparison between these techniques, leading to concrete evidence that the ABL technique is more efficient.

Keywords: factor mimicking, portfolio construction, Augmented Black-Litterman (ABL), factor tilt, Fama-French, factor ranking, factor risk model, optimisation, OLS, GLS

JEL Classification: C10, C11, C61, G11


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Date posted: October 26, 2009 ; Last revised: January 25, 2016

Suggested Citation

Cheung, Wing and Mittal, Nikhil, Efficient Bayesian Factor Mimicking: Methodology, Tests and Comparison (October 18, 2009). Available at SSRN: http://ssrn.com/abstract=1457022 or http://dx.doi.org/10.2139/ssrn.1457022

Contact Information

Wing Cheung (Contact Author)
Independent ( email )
No Address Available
Nikhil Mittal
Independent ( email )
No Address Available
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