Open Access Journal Article

A Study of Hierarchical Risk Parity in Portfolio Construction

by Debjani Palit a,* orcid  and  Victor R. Prybutok b orcid
a
Department of Information Science, University of North Texas, Denton, USA
b
Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, USA
*
Author to whom correspondence should be addressed.
JEA  2024, 68; 3(3), 68; https://doi.org/10.58567/jea03030006
Received: 22 July 2023 / Accepted: 4 September 2023 / Published Online: 15 September 2024

Abstract

The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.


Copyright: © 2024 by Palit and Prybutok. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Palit, D.; Prybutok, V. R. A Study of Hierarchical Risk Parity in Portfolio Construction. Journal of Economic Analysis, 2024, 3, 68. https://doi.org/10.58567/jea03030006
AMA Style
Palit D, Prybutok V R. A Study of Hierarchical Risk Parity in Portfolio Construction. Journal of Economic Analysis; 2024, 3(3):68. https://doi.org/10.58567/jea03030006
Chicago/Turabian Style
Palit, Debjani; Prybutok, Victor R. 2024. "A Study of Hierarchical Risk Parity in Portfolio Construction" Journal of Economic Analysis 3, no.3:68. https://doi.org/10.58567/jea03030006
APA style
Palit, D., & Prybutok, V. R. (2024). A Study of Hierarchical Risk Parity in Portfolio Construction. Journal of Economic Analysis, 3(3), 68. https://doi.org/10.58567/jea03030006

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References

  1. Ardia, D., Bolliger, G., Boudt, K. & Gagnon-Fleure, J.P. (2017). The impact of covariance misspecification in risk-based portfolios. Annals of Operations Research 254, 1–16. https://doi.org/10.1007/s10479-017-2474-7
  2. Bernartzi, S. & Thaler, R.H. (2001). Naïve diversification strategies in defined contribution saving plans. American Economic Review 91(1), 79-98. https://doi.org/10.1257/aer.91.1.79
  3. Braga, M.D. (2015). Risk-based approaches to asset allocation: concepts and practical applications. Springer.
  4. Bruder, B. & Roncalli, T. (2012). Managing risk exposures using the risk budgeting approach. Risk Management e Journal. https://doi.org/10.2139/ssrn.2009778
  5. Demey, P., Maillard, S., & Roncalli, T. (2010). Risk-based indexation. Available at SSRN 1582998. https://doi.org/10.2139/ssrn.1582998
  6. DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus Naive Diversification: How inefficient Is the 1/n Portfolio Strategy? Review of Financial Studies 22(5), 1915-1953. http://dx.doi.org/10.1093/rfs/hhm075
  7. Green, R.C. & Hollifield, B. (1992). When will mean-variance efficient portfolios be well diversified? The Journal of Finance 47(5), 1785-1809. https://doi.org/10.1111/j.1540-6261.1992.tb04683.x
  8. Hult, H., Lindskog, F., Hammarlid, O., & Rehn, C. J. (2012). Risk and Portfolio Analysis Principles and Methods, Springer, New York. https://doi.org/10.1007/978-1-4614-4103-8
  9. Illmanen, A., & Villalon, D. (2012). Alpha Beyond Expected Returns. Portfolio Constructions 1-12. https://www.aqr.com/Insights/Research/White-Papers/Alpha-Beyond-Expected-Returns
  10. Jagannathan, R., & Ma, T. (2003). Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps. Journal of Finance 58(4), 1651-1684. https://www.jstor.org/stable/3648224
  11. Kolm, P.N., Tütüncü, R. & Fabozzi, F.J. (2014). 60 Years of Portfolio Optimization: Practical Challenges and Current Trends. European Journal of Operational Research 234, 356-371. https://doi.org/10.1016/j.ejor.2013.10.060
  12. López de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons.
  13. Maillard, S., Roncalli, T., & Teiletche, J. (2010). On the Properties of Equally - Weighted Risk Contributions Portfolios. The Journal of Portfolio Management 36(4), 60-70. http://dx.doi.org/10.3905/jpm.2010.36.4.060
  14. Maimon, O., & Rokach, L. (2010). Introduction to knowledge discovery and data mining. In Data mining and knowledge discovery handbook 1-15. Boston, MA: Springer US.
  15. Manning C.D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. http://informationretrieval.org/
  16. Markowitz, H.M. (1952). Portfolio Selection. Journal of Finance 7(1), 77-91. https://doi.org/10.2307/2975974
  17. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics 8, 323-361. https://doi.org/10.1016/0304-405X(80)90007-0
  18. Munk, C. (2018). Financial Markets and Investment.
  19. https://www.academia.edu/37331452/Financial_Markets_and_Investments
  20. Murtagh, F., & Contreras, P. (2017). Algorithms for hierarchical clustering: an overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7. https://doi.org/10.1002/widm.1219
  21. Qian, E.Y. (2005). Risk Parity Portfolios: Efficient Portfolios Through True Diversification. PanAgora Asset Management.https://www.panagora.com/assets/PanAgora-Risk-Parity-Portfolios-Efficient-Portfolios-Through-True-Diversification.pdf
  22. Roncalli, T. (2013). Introduction to Risk Parity and Budgeting. CRC Press, Boca Raton.
  23. https://doi.org/10.2139/ssrn.2272973
  24. Rubinstein, M. (2002). Markowitz Portfolio Selection: A Fifty-Year Retrospective. The Journal of Finance 57(3), 1041-1045. https://doi.org/10.1111/1540-6261.00453
  25. Scherer, B. (2007). Portfolio construction and risk budgeting. Edhec Business School 5.
  26. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance 19(23), 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
  27. Steinbach, M. C. (2001). Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis. SIAM Review 43(1), 31-85. http://dx.doi.org/10.1137/S0036144500376650
  28. Windcliff, H. & Boyle, P. (2004). The 1/n Pension Investment Puzzle. North American Actuarial Journal 8(1), 32-45. https://doi.org/10.1080/10920277.2004.10596151