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Risk Parity Stock Optimization Using Principal Component Quantile Simulation

Fagerholt, Anders Loe; Aanonsen, Bård Ø.
Master thesis
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URI
http://hdl.handle.net/11250/2423114
Date
2016
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  • Institutt for industriell økonomi og teknologiledelse [1897]
Abstract
Today's portfolio optimization models are often too sensitive to stochastic

input parameters and the use of outdated risk measures, resulting in poor

risk adjusted return. This thesis presents a solution to avoid these diculties

by introducing a new simulation framework to obtain multivariate return

distributions for correlated assets. Next a model to obtain a risk parity

portfolio using Conditional Value at Risk (CVaR) is oered making the model

able to capture asset specic risk characteristics present in the tails of the

marginal distributions.

Quantile regression and principal component analysis (PCA) are combined

to form a factor model able to capture the entire return distribution

and maintain dependencies between correlated assets. A new method to

simulate future principal components is presented making the simulation algorithm

quick and eective.

The resulting marginal distributions show asset spesic risk characteristics

and tail behaviour. This is in turn re

ected in the risk parity portfolio

weights, conrming CVaR as a risk measure oering better intelligence to

investors.
Publisher
NTNU

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