Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2017
Abstract
The authors show that risk aversion and prior estimation error input parameters of the Black-Litterman model that are arbitrarily fixed in existing practices should instead be carefully calibrated because they are related to the Sharpe performance ratio and Value at Risk or tail risk of the active portfolio. A related important insight is that these parameters are not entirely exogenous but are connected closely to the portfolio manager's inputs of subjective expected returns, as well as the degree of confidence over these subjective beliefs. The value of τ is closer to zero if the manager believes the initial estimates based on historical data are accurate compared to the subjective views and closer to one if the manager believes there is a fundamental shift in the market landscape such that past history should not be overly relied upon. The authors also show that in the event of an incorrect view, an unrealistically high Sharpe ratio and excessive risk taking can produce disastrous losses. Unifying parameter calibrations with performance and risk measures, the model is internally consistent and provides a powerful means for practical application.
Discipline
Finance and Financial Management | Portfolio and Security Analysis
Research Areas
Finance; Quantitative Finance
Publication
Journal of Portfolio Management
Volume
43
Issue
3
First Page
126
Last Page
135
ISSN
0095-4918
Identifier
10.3905/jpm.2017.43.3.126
Publisher
Institutional Investor Inc
Citation
TEE, Chyng Wen; HUANG, Shirley; and Kian Guan LIM.
Performance control and risk calibration in the Black-Litterman model. (2017). Journal of Portfolio Management. 43, (3), 126-135.
Available at: https://ink.library.smu.edu.sg/lkcsb_research_all/5
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3905/jpm.2017.43.3.126