HRP performance comparison in portfolio optimization under various codependence and distance metrics

Illya Barziy and Marcin Chlebus ()

No 2020-21, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: Problem of portfolio optimization was formulated almost 70 years ago in the works of Harry Markowitz. However, the studies of possible optimization methods are still being provided in order to obtain better results of asset allocation using the empirical approximations of codependences between assets. In this work various codependences and metrics are tested in the Hierarchical Risk Parity algorithm to determine whether the results obtained are superior to those of the standard Pearson correlation as a measure of codependence. In order to compare how HRP uses the information from alternative codependence metrics, the MV, IVP, and CLA optimization algorithms were used on the same data. Dataset used for comparison consisted of 32 ETFs representing equity of different regions and sectors as well as bonds and commodities. The time period tested was 01.01.2007-20.12.2019. Results show that alternative codependence metrics show worse results in terms of Sharpe ratios and maximum drawdowns in comparison to the standard Pearson correlation for each optimization method used. The added value of this work is using alternative codependence and distance metrics on real data, and including transaction costs to determine their impact on the result of each algorithm.

Keywords: Hierarchical Risk Parity; portfolio optimization; ETF; hierarchical structure; clustering; backtesting; distance metrics; risk management; machine learning (search for similar items in EconPapers)
JEL-codes: C32 C38 C44 C51 C52 C61 C65 G11 G15 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-21

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