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Beyond risk parity – A machine learning-based hierarchical risk parity approach on cryptocurrencies

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  • Burggraf, Tobias

Abstract

It has long been known that estimating large empirical covariance matrices can lead to very unstable solutions, with estimation errors more than offsetting the benefits of diversification. In this study, we employ the Hierarchical Risk Parity approach, which applies state-of-the-art mathematics including graph theory and unsupervised machine learning to a large portfolio of cryptocurrencies. An out-of-sample comparison with traditional risk-minimization methods reveals that Hierarchical Risk Parity outperforms in terms of tail risk-adjusted return, thereby working as a potential risk management tool that can help cryptocurrency investors to better manage portfolio risk. The results are robust to different rebalancing intervals, covariance estimation windows and methodologies.

Suggested Citation

  • Burggraf, Tobias, 2021. "Beyond risk parity – A machine learning-based hierarchical risk parity approach on cryptocurrencies," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s154461232030177x
    DOI: 10.1016/j.frl.2020.101523
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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    3. John L. Evans & Stephen H. Archer, 1968. "Diversification And The Reduction Of Dispersion: An Empirical Analysis," Journal of Finance, American Finance Association, vol. 23(5), pages 761-767, December.
    4. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    5. Ahmed, Shaker & Grobys, Klaus & Sapkota, Niranjan, 2020. "Profitability of technical trading rules among cryptocurrencies with privacy function," Finance Research Letters, Elsevier, vol. 35(C).
    6. Statman, Meir, 1987. "How Many Stocks Make a Diversified Portfolio?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(3), pages 353-363, September.
    7. Grobys, Klaus & Ahmed, Shaker & Sapkota, Niranjan, 2020. "Technical trading rules in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 32(C).
    8. Bouri, Elie & Shahzad, Syed Jawad Hussain & Roubaud, David, 2019. "Co-explosivity in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 29(C), pages 178-183.
    9. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    10. Philipp J. Kremer & Andreea Talmaciu & Sandra Paterlini, 2018. "Risk minimization in multi-factor portfolios: What is the best strategy?," Annals of Operations Research, Springer, vol. 266(1), pages 255-291, July.
    11. Kim, Thomas, 2017. "On the transaction cost of Bitcoin," Finance Research Letters, Elsevier, vol. 23(C), pages 300-305.
    12. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.
    13. Baur, Dirk G. & Dimpfl, Thomas & Kuck, Konstantin, 2018. "Bitcoin, gold and the US dollar – A replication and extension," Finance Research Letters, Elsevier, vol. 25(C), pages 103-110.
    14. Platanakis, Emmanouil & Urquhart, Andrew, 2019. "Portfolio management with cryptocurrencies: The role of estimation risk," Economics Letters, Elsevier, vol. 177(C), pages 76-80.
    15. Liu, Weiyi, 2019. "Portfolio diversification across cryptocurrencies," Finance Research Letters, Elsevier, vol. 29(C), pages 200-205.
    16. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis," Finance Research Letters, Elsevier, vol. 29(C), pages 68-74.
    17. Corbet, Shaen & Eraslan, Veysel & Lucey, Brian & Sensoy, Ahmet, 2019. "The effectiveness of technical trading rules in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 31(C), pages 32-37.
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    Cited by:

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    2. Lin Li, 2023. "Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(4), pages 255-267, July.
    3. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).

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    More about this item

    Keywords

    Machine learning; Graph theory; Hierarchical tree clustering; Asset allocation; Cryptocurrencies;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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