论文标题
使用混合矢量自回旋模型的投资组合优化
Portfolio optimization with mixture vector autoregressive models
论文作者
论文摘要
获得条件协方差矩阵的可靠估计是异性多元时间序列的重要任务。在投资组合优化和财务风险管理中，至关重要的是尽可能准确地提供不确定性和风险的措施。我们建议使用混合矢量自回旋（MVAR）模型进行投资组合优化。 MVAR模型结合了取决于该过程的最新历史的分布的混合物，可以在多变量时间序列数据中适应不对称性，多模式，异性恋和互相关。对于正常组件的混合物，我们利用多元正态分布的特性获得了资产投资组合上收益的条件预测分布的明确公式。在展示了该方法的工作原理后，我们与其他相关的多元时间序列模型进行了比较。
Obtaining reliable estimates of conditional covariance matrices is an important task of heteroskedastic multivariate time series. In portfolio optimization and financial risk management, it is crucial to provide measures of uncertainty and risk as accurately as possible. We propose using mixture vector autoregressive (MVAR) models for portfolio optimization. Combining a mixture of distributions that depend on the recent history of the process, MVAR models can accommodate asymmetry, multimodality, heteroskedasticity and cross-correlation in multivariate time series data. For mixtures of Normal components, we exploit a property of the multivariate Normal distribution to obtain explicit formulas of conditional predictive distributions of returns on a portfolio of assets. After showing how the method works, we perform a comparison with other relevant multivariate time series models on real stock return data.
