Portfolio Optimization with Trend Following Strategies

dc.contributor.authorRubenson, Samuel
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:24:46Z
dc.date.available2019-07-03T14:24:46Z
dc.date.issued2016
dc.description.abstractThis thesis investigates how the mean-variance framework for portfolio optimization compares against that of risk-parity and the minimum conditional value-at-risk (CVaR) portfolio. Within the risk measure of portfolio variance, we find that the performance of the mean-variance portfolio is highly dependent on a well-conditioned sample covariance matrix while risk-parity appears to offer increased numerical stability. But with a regularized estimate, no method consistently outperforms the other. We suggest a minor extension to the risk-parity allocation objective with a resulting portfolio that exhibits superior properties in several central aspects. The minimum CVaR portfolio is built around the alternative risk measure conditional value-at-risk and we find that while the original problem formulation is prone to overfitting, a regularized version shows promising results worthy of further investigation.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/246902
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectGrundläggande vetenskaper
dc.subjectData- och informationsvetenskap
dc.subjectBasic Sciences
dc.subjectComputer and Information Science
dc.titlePortfolio Optimization with Trend Following Strategies
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeDatateknik 300 hp (civilingenjör)
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