Analytic methods for asset allocation with illiquid investments and low-frequency data
Name
1076344627-MIT.pdf
Description
Full printable version
Size
24.61 MB
Format
Adobe PDF
Checksum (MD5)
8b243f5ff69aafc2b24063a2f6eba46d
Author(s)
Slakter, Adam R
Advisor(s)
Peter Kempthorne and Ray Ming Yeh.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Investing in illiquid assets poses a challenge to investors, as the low-frequency data makes it difficult to quantify the risks across portfolios and make asset allocation decisions. This work reviews several principal methods to infer missing data and tests their implications for asset allocation. It compares these methods by applying them to hypothetical portfolios in a realistic simulation environment, helping allocators decide which methodology to use and when. Proxy-based methods, which utilize a related series of higher-frequency observations, outperform non proxy-based inference techniques when the correlation of the available proxy is above 0.3. If data autocorrelation is high, models such as Kalman filters, which are capable of explicitly modeling the autocorrelation outperform other proxy-based methods. In normal market conditions, the CL Method gives the best overall performance of methods tested, indicated by low RMSEs and reliable forecasts for mean return, volatility, Sharpe Ratio, and drawdown. Keywords: Illiquid Investments, Low-Frequency Data, Missing Data.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 89-90).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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