Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi‐asset Multi‐factor Allocations

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

doi:10.1002/9781119751182.ch9

Cited by 21 publications

(6 citation statements)

References 17 publications

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“…This is accomplished by recursively bisecting the rearranged covariance matrix. In particular, in the original paper an IVP approach is applied to assets within a cluster, but alternatives are possible (Lopez de Prado 2016; Lohre et al 2020). An implementation of this scheme is available in Martin (2021).…”

Section: Inverse Variance Portfolio (Ivp)mentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

Ferretti

2022

Comput Econ

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We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson’s correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

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Section: Inverse Variance Portfolio (Ivp)mentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

Ferretti

2022

Comput Econ

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“…This is accomplished by recursively bisecting the rearranged covariance matrix. In particular, in the original paper an IVP approach is applied to assets within a cluster, but alternatives are possible [43,31]. An implementation of this scheme is available in [34].…”

Section: Hierarchical Risk Parity (Hrp)mentioning

confidence: 99%

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Ferretti

2022

Preprint

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We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal.

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“…To account for the nonstationarity of futures return time series, we generate an additional dataset of time series by block bootstrapping (Hall 1985;Carlstein 1986;Fengler and Schwendner 2004;Lohre, Rother, and Schaefer 2020): § Blocks with a fixed length but a random starting point in time are defined from the futures return time series. One block corresponds to 60 business days.…”

Section: Robustness Of the Strategies-bootstrapped Datasetmentioning

confidence: 99%

Interpretable Machine Learning for Diversified Portfolio Construction

Jaeger¹,

Krügel²,

Marinelli³

et al. 2021

JFDS

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The authors introduce a procedure to benchmark rule-based investment strategies and to explain the differences in path-dependent risk-adjusted performance measures using interpretable machine learning.n They apply the procedure to the Calmar ratio spread between hierarchical risk parity (HRP) and equal risk contribution (ERC) allocations of a multi-asset futures portfolio and find HRP to have superior risk-adjusted performance.n The authors regress the Calmar ratio spread against statistical features of bootstrapped futures return datasets using XGBoost and apply the SHAP framework by Lundberg and Lee (2017) to discuss the local and global feature importance.

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Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi‐asset Multi‐factor Allocations

Cited by 21 publications

References 17 publications

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection

On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection

Interpretable Machine Learning for Diversified Portfolio Construction

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