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Article

Identifying Economic Regimes: Reducing Downside
Risks for University Endowments and Foundations

John M. Mulvey and Han Liu
The Journal of Portfolio Management Fall 2016, 43 (1) 100-108; DOI: https://doi.org/10.3905/jpm.2016.43.1.100
John M. Mulvey
is a professor in the Operations Research and Financial Engineering Department and a founding member of the Bendheim Center for Finance at Princeton University in Princeton, NJ.
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  • For correspondence: mulvey@princeton.edu
Han Liu
is an assistant professor in the Operations Research and Financial Engineering Department and a member of the Statistics and Machine Learning Center at Princeton University in Princeton, NJ.
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  • For correspondence: hanliu@princeton.edu
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Abstract

One of the most durable patterns in market behavior involves contagion—increases in correlation and volatility—during crash periods such as 2008. This condition can cause major problems for an investor when markets severely contract and anticipated diversification benefits vanish. To address contagion, the authors implement a machine-learning algorithm, trend filtering, to capture distinctive economic conditions. Over long horizons, they find that a multiregime simulation provides more accurate estimates of downside risk compared with traditional static portfolio models and can help in evaluating strategies for reducing the worst-case outcomes. The approach readily applies to nonprofit institutions that depend upon their endowment capital to fund liabilities and meet goals.

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The Journal of Portfolio Management: 43 (1)
The Journal of Portfolio Management
Vol. 43, Issue 1
Fall 2016
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Identifying Economic Regimes: Reducing Downside
Risks for University Endowments and Foundations
John M. Mulvey, Han Liu
The Journal of Portfolio Management Oct 2016, 43 (1) 100-108; DOI: 10.3905/jpm.2016.43.1.100

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Identifying Economic Regimes: Reducing Downside
Risks for University Endowments and Foundations
John M. Mulvey, Han Liu
The Journal of Portfolio Management Oct 2016, 43 (1) 100-108; DOI: 10.3905/jpm.2016.43.1.100
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