Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

Lohre, Harald; Rother, Carsten; Schäfer, Kilian Axel

doi:10.2139/ssrn.3513399

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Hierarchical Equal Risk Contribution Portfolio (HERC) (Raffinot 2018) merges HRP and HCAA . Several variations to this approach have also been proposed (Lohre et al 2020;Molyboga 2020). In particular, we use the HRP model as a benchmarking method to compare our proposal, as explained below.…”

Section: Portfolio Selectionmentioning

confidence: 99%

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Lorenzo

Arroyo

2023

Financ Innov

View full text Add to dashboard Cite

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates, which may result in poor out-of-sample performance. In particular, the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long. This is precisely the case in the cryptocurrency market, where there are hundreds of crypto assets that have been traded for a few years. We propose enhancing the mean-variance (MV) model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period. In the pre-selection stage, we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality. The prototypes of the clustering partition are automatically examined and the one that best suits our risk-aversion preference is selected. We then run the MV portfolio optimization with the crypto assets of the selected cluster. The proposed approach is tested for a period of 17 months in the whole cryptocurrency market and two selections of the cryptocurrencies with the higher market capitalization (175 and 250 cryptos). We compare the results against three methods applied to the whole market: classic MV, risk parity, and hierarchical risk parity methods. We also compare our results with those from investing in the market index . The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators. This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

show abstract

Section: Portfolio Selectionmentioning

confidence: 99%

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Lorenzo

Arroyo

2023

Financ Innov

View full text Add to dashboard Cite

show abstract

“…Regarding all the mentioned good wills, implementation and theories shows the dedicated movement of price of cryptocurrency market. Lahre et al [11] propose the strategy of Hierarchical Risk Parity (HRP) on the multi-asset multi-factor allocation which achieves the good results on tail risk. Moreover, Jain et al [12] applied the same strategy for the individual stocks to comport the fifty indexes of NIFTY.…”

Section: Trillion Dollar Of Trading In Thismentioning

confidence: 99%

Machine Learning-Based Analysis of Cryptocurrency Market Financial Risk Management

Shahbazi

Byun

2022

IEEE Access

View full text Add to dashboard Cite

Cryptocurrency is one of the famous financial state in all over the world which cause several type of risks that effect on the intrinsic assessment of risk auditors. From the beginning the growth of cryptocurrency gives the financial business with the wide risk in term of presentation of money laundering. In the institution of financial supports such as anti-money laundering, banks and secrecy of banks proceed as a specialist of risk, manager of bank and officer of compliance which has a provocation for the related transaction through cryptocurrency and the users who hide the illegal funds.In this study, the Hierarchical Risk Parity and unsupervised machine learning applied on the cryptocurrency framework. The process of professional accounting in term of inherent risk connected with cryptocurrency regarding the occurrence likelihood and statement of financial impact. Determining cryptocurrency risks comprehended to have a high rate of occurrence likelihood and the access of private key which is unauthorized. The professional cryptocurrency experience in transaction cause the lower risk comparing the less experienced one. The Hierarchical Risk Parity gives the better output in term of returning the adjusted risk tail to get the better risk management result.The result section shows the proposed model is robust to various intervals which are re-balanced and the co-variance window estimation.INDEX TERMS Risk management, cryptocurrency, inherent risk, ineffective exchange control.

show abstract

A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets

Marti¹,

Nielsen

Bińkowski

et al. 2021

Signals and Communication Technology

View full text Add to dashboard Cite

Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

Cited by 3 publications

References 16 publications

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm

Machine Learning-Based Analysis of Cryptocurrency Market Financial Risk Management

A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets

Contact Info

Product

Resources

About