PERBANDINGAN OPTIMISASI PORTOFOLIO METODE MEAN- VARIANCE DENGAN METODE MEAN-SEMIVARIANCE
In 1952 Markowitz pioneered the use of Mean-Variance method for portfolio optimization problems, for which Mean-Variance method is very popular to use. However, Mean-Variance method has drawbacks that the return data should be normal distributed. In fact, it is very difficult to get data that has no...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2013
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/123842/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=63958 |
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| Institution: | Universitas Gadjah Mada |
| Summary: | In 1952 Markowitz pioneered the use of Mean-Variance method for portfolio
optimization problems, for which Mean-Variance method is very popular to use.
However, Mean-Variance method has drawbacks that the return data should be
normal distributed. In fact, it is very difficult to get data that has normal
distributed return.
Markowitz (1959) argued that �analysis based on semivariance tend to produce
better portfolios than those based on variance�. However, why is the analysis of
the portfolio with Mean-Variance more often used than Mean-Semivariance? This
is because, unlike covariance matrix that is symmetric and exogenous,
semicovariance matrix is asymmetric and endogenous. Thus in calculating the
weights, numerical algorithms must be used that is rarely used by practitioners
and academics. Therefore, heuristic approach used which fuction to change
semicovariance matrix to be symmetric and exogenous. So calculating the weights
of Mean-Semivariance portfolio could use the same with Mean-Variance
portfolio.
Portfolio optimization using Mean-Semivariance does not require any distribution
assumptions, making it much easier to use than Mean-Variance. The calculations
are easy and with heuristic approach obtained semicovariance matrix which has
the same form and finishing with covariance matrix of Mean-Variance.
In this thesis the empirical comparison will be made between Mean-Semivariance
portfolio optimization with Mean-Variance portfolio optimization. Then in case
studies portfolio formation carried out Mean-Variance portfolio and Mean-
Semivariance portfolio with combination of multiple financial assets. |
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