Stability of Traditional Portfolio Models

dc.contributor.authorSharma, Ruben
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T13:36:34Z
dc.date.available2019-07-03T13:36:34Z
dc.date.issued2015
dc.description.abstractThe idea behind this thesis came from, at that time, a fellow colleague at SEB. They had evaluated the resampling model when doing portfolio optimizations and knew that it was more stable than the original model by Markowitz. What they hadn’t studied, was to what extent and also the instability of the resampling model itself. Therefore the research questions for this thesis were • Research question 1: How sensitive are the two models’ optimal portfolio weights to changes in expected return, risk and correlation. • Research question 2: How does the inherit portfolio characteristic affect the results of research question 1. To study these questions reference portfolios were derived based on historical asset returns of several multi asset portfolios at different risk levels. To investigate the instability of the models the input parameters were stressed in different combinations to see how the portfolios weights changed. In short, the Markowitz model was more unstable then the resampling model, the dispersion increases with portfolio risk level and the expected return parameter is the parameter that has the largest impact on stability for both models. A few exceptions was seen on the upper end of the risk scale. The results in this study confirms the result of previous studies but also challenges other.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/213466
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectGrundläggande vetenskaper
dc.subjectMatematik
dc.subjectBasic Sciences
dc.subjectMathematics
dc.titleStability of Traditional Portfolio Models
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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