Description
- Abstract:
- Motivated largely by the problems of estimation error in investment portfolio optimization, this dissertation develops techniques to improve covariance forecasting and portfolio construction. Covariance forecasting is improved by incorporating contemporaneous side information and by propagating uncertainty of the parameters in factor-modeled covariance to uncertainty of the covariance matrix itself. The latter makes it possible to approach several investment problems explicitly modeling uncertainty. Markowitz mean-variance portfolio optimization becomes robust optimization with an uncertainty set on portfolio utility. Uncertain risk parity considers deviation from the risk budget. Risk under uncertainty and price movement measures the stability of hedges under forecasting uncertainty and shifts in risk exposure caused by dynamic prices.
- Notes:
- Thesis (Ph. D.)--Brown University, 2023
Citation
Shah, Anish Rasiklal,
"Improving Portfolio Construction through Side Information and Quantified Uncertainty"
(2023).
Applied Mathematics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:b2bc42vj/
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Applied Mathematics Theses and Dissertations
Theses and Dissertations for the Applied Mathematics department....