Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers. Furthermore, to better reflect cross-sectional correlations, we introduce a correlation-guided regularizer informed by a stable estimate of the target correlation matrix. This structure effectively extracts salient features not only from historical returns but also from asset-specific and systematic covariates, significantly enhancing the performance of forecasts and portfolios. Experimental results on the daily excess returns of 12 industry portfolios show that Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance. Moreover, in portfolio experiments, portfolios constructed from Diffolio's forecasts show consistently robust performance, thereby outperforming those from benchmarks by achieving higher Sharpe ratios for the mean-variance tangency portfolio and higher certainty equivalents for the growth-optimal portfolio. These results demonstrate the superiority of our proposed Diffolio in terms of not only statistical accuracy but also economic significance.

翻译：概率预测对于构建考虑复杂横截面依赖性的高效投资组合在多变量金融时间序列中至关重要。本文提出Diffolio，一种专为多变量金融时间序列预测与投资组合构建设计的扩散模型。Diffolio采用具有分层注意力架构的去噪网络，包含资产级与市场级两个层次。此外，为更好反映横截面相关性，我们引入由目标相关矩阵稳定估计指导的相关性引导正则化器。该结构不仅能从历史收益率中提取显著特征，还能从资产特定协变量与系统性协变量中提取关键信息，显著提升预测与投资组合的性能。基于12个行业投资组合的每日超额收益率实验表明，Diffolio在多变量预测准确性与投资组合表现上优于多种概率预测基线方法。此外，在投资组合实验中，由Diffolio预测构建的投资组合展现出持续稳健的表现，其均值-方差切点投资组合获得更高夏普比率，增长最优投资组合获得更高确定性等价水平，从而超越基准方法。这些结果证明了所提Diffolio在统计准确性与经济显著性上的双重优势。