论文标题
带有凸的非负矩阵分解的风险预算投资组合
Risk budget portfolios with convex Non-negative Matrix Factorization
论文作者
论文摘要
我们提出了一种基于凸面非负矩阵分解（NMF）的风险因素预算的投资组合分配方法。与经典因素分析，PCA或ICA不同，NMF确保积极因素负载以获得可解释的长期投资组合。由于NMF因素代表了单独的风险来源，因此它们具有准二基因相关矩阵，从而促进了多样化的投资组合分配。我们在针对两个仅长期全球的加密货币和传统资产的全球投资组合的背景下评估了我们的方法。我们的方法比有关多元化的经典投资组合分配优于与分层风险平价（HRP）更好的风险概况。我们使用Monte Carlo模拟评估发现的鲁棒性。
We propose a portfolio allocation method based on risk factor budgeting using convex Nonnegative Matrix Factorization (NMF). Unlike classical factor analysis, PCA, or ICA, NMF ensures positive factor loadings to obtain interpretable long-only portfolios. As the NMF factors represent separate sources of risk, they have a quasi-diagonal correlation matrix, promoting diversified portfolio allocations. We evaluate our method in the context of volatility targeting on two long-only global portfolios of cryptocurrencies and traditional assets. Our method outperforms classical portfolio allocations regarding diversification and presents a better risk profile than hierarchical risk parity (HRP). We assess the robustness of our findings using Monte Carlo simulation.
