Portfolio Optimization with Trend Following Strategies

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/246902
Download file(s):
File Description SizeFormat 
246902.pdfFulltext1.24 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Portfolio Optimization with Trend Following Strategies
Authors: Rubenson, Samuel
Abstract: This 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.
Keywords: Grundläggande vetenskaper;Data- och informationsvetenskap;Basic Sciences;Computer and Information Science
Issue Date: 2016
Publisher: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper
Chalmers University of Technology / Department of Mathematical Sciences
URI: https://hdl.handle.net/20.500.12380/246902
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.