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A critical review of the global minimum variance theory

Claeson, Martin LU (2016) STAH11 20152
Department of Statistics
Abstract
The main purpose of this thesis is to give a basic understanding of the GMV portfolio theory and the problematics that arise when using the sample covariance matrix as the only parameter. The reason for this is the amount of estimation error that tends to increase as the sample covariance matrix goes to a higher dimension. In an attempt to reduce the amount of error, an alternative approach based on sector indices is introduced, which gives new and interesting results. This is a useful approach, since we are explaining the chosen stocks with fewer time series, a smaller dimension of covariance matrix needs to be estimated. This thesis lay the ground for this basic strategy, however, before any more profound conclusions can be drawn,... (More)
The main purpose of this thesis is to give a basic understanding of the GMV portfolio theory and the problematics that arise when using the sample covariance matrix as the only parameter. The reason for this is the amount of estimation error that tends to increase as the sample covariance matrix goes to a higher dimension. In an attempt to reduce the amount of error, an alternative approach based on sector indices is introduced, which gives new and interesting results. This is a useful approach, since we are explaining the chosen stocks with fewer time series, a smaller dimension of covariance matrix needs to be estimated. This thesis lay the ground for this basic strategy, however, before any more profound conclusions can be drawn, further investigations have to be made. (Less)
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author
Claeson, Martin LU
supervisor
organization
course
STAH11 20152
year
type
M2 - Bachelor Degree
subject
language
English
id
8884891
date added to LUP
2016-09-22 14:25:39
date last changed
2016-09-22 14:25:39
@misc{8884891,
  abstract     = {The main purpose of this thesis is to give a basic understanding of the GMV portfolio theory and the problematics that arise when using the sample covariance matrix as the only parameter. The reason for this is the amount of estimation error that tends to increase as the sample covariance matrix goes to a higher dimension. In an attempt to reduce the amount of error, an alternative approach based on sector indices is introduced, which gives new and interesting results. This is a useful approach, since we are explaining the chosen stocks with fewer time series, a smaller dimension of covariance matrix needs to be estimated. This thesis lay the ground for this basic strategy, however, before any more profound conclusions can be drawn, further investigations have to be made.},
  author       = {Claeson, Martin},
  language     = {eng},
  note         = {Student Paper},
  title        = {A critical review of the global minimum variance theory},
  year         = {2016},
}