论文标题
关于投资组合分配方案的建模和模拟：基于网络社区检测的方法
On the Modeling and Simulation of Portfolio Allocation Schemes: an Approach based on Network Community Detection
论文作者
论文摘要
我们介绍了有关财务应用投资组合投资的研究。我们描述了一个通用的建模和仿真框架，并研究对使用不同指标来衡量资产之间相关性的影响。特别是，除了传统的皮尔森相关性外，我们还采用了偏置的互相关分析（DCCA）和降解的部分互相关分析（DPCCA）。此外，引入了一种新型的投资组合分配方案，该方案将资产视为一个复杂的网络，并使用模块化来检测相关资产的社区。为了多样化，分配的权重分配在不同社区之间。模拟将这种新型方案与临界线算法（CLA），逆差异组合（IVP），分层风险平等（HRP）进行比较。合成时间系列是使用高斯模型，几何布朗运动，Garch，Arfima和改良的Arfima模型生成的。结果表明，在许多情况下，所提出的方案优于艺术的方法。我们还通过进行回测验证了仿真结果，其结果证实了该提案的生存能力。
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.
