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Activity Number:
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440
- SLDS CSpeed 8
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #319124
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Title:
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Optimal Financial Portfolio Using Graphical Lasso Under Unstable Environment
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Author(s):
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Ekaterina Seregina* and Tae-Hwy Lee
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Companies:
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University of California, Riverside and University of California, Riverside
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Keywords:
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Time-Varying Portfolio;
Common Factors;
Structural Break;
Graphical Lasso;
ADMM
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Abstract:
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Unstable environments raise challenges for constructing a financial portfolio. In such scenarios, it is unrealistic to assume constant portfolio weights, whereas estimating weights using only post-break observations omits the information prior to the break point. This paper visualizes stock returns as a network of interacting entities and generalizes network inference in the presence of structural breaks. We estimate time-varying portfolio weights using pre- and post-break data when the stock returns are driven by common factors. Using the example of a strong structural break caused by the first wave of COVID-19 pandemic, we demonstrate that combining pre- and post-break observations for estimating portfolio weights improves portfolio return and Sharpe Ratio compared to constant weights and weights that use only post-break observations.
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Authors who are presenting talks have a * after their name.
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