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On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach Based on Network Community Detection by Stefano Ferretti is a Economics, Econometrics and Finance article available to read on EtoBox.

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## Abstract 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.

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

Author

Stefano Ferretti

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Economics, Econometrics and Finance (Social Sciences)