EPHA DIANA SUPANDI (1); Prof. Dr.rer.nat. Dedi Rosadi, S.Si.,, M.Sc. (2); Prof. Dr. Abdurakhman, S.Si., M.Si. (3)
Tanggal/Date
1 2017
Kata Kunci/Keyword
Abstrak/Abstract
A robust optimization has emerged as a powerful tool for managing uncertainty in many optimization problems. This method was adapted in
portfolio optimization to resolve the sensitivity issue of the mean-variance
model to its inputs (i.e. mean vector and covariance matrix of returns).
The solution provided by this framework presented here can be very
sensitive to the choice of uncertainty sets, since the optimal portfolios
are determined under "the worst-case objective value" of the inputs in
their uncertainty sets. One potential consequence of this emphasis on
the worst-case is that the decisions are highly influenced by extreme scenarios in the uncertainty sets. The emergence of the extreme scenarios
in the uncertainty sets can be because there are extreme observations in
the data. These extreme observations frequently occur in financial sector.
We proposed to tackle this issue by considering robust estimators that are
incorporated to the uncertainty sets about unknown parameters. They
showed both in simulated and empirical investigations that this strategy
can lead to the construction of portfolios with superior out-of-sample
performance in comparison to the mean-variance portfolio (classic) and
robust portfolio optimization.
