We propose a conditional correlation model with long memory dependence and smooth structural change. Previous literature has considered correlation and covariance models with structural change or long memory, but this is the first paper to jointly model both features. The correlation matrix is decomposed into long and short run components. Short run correlations converge hypergeometrically towards a slow moving long run correlation matrix that evolves according to one or more flexible Fourier forms. The model is applied to two data sets: a US equity portfolio; and a US equity, bond, gold and oil portfolio. Model fit and out of sample forecasts over 1 to 60 day horizons support the proposed approach.

我们提出了一种具有长记忆依赖性和平滑结构变化的条件相关模型。先前的文献考虑了具有结构变化或长记忆的相关性和协方差模型，但这是第一篇对这两种特征进行联合建模的论文。相关矩阵被分解为长期和短期分量。短期相关性超几何地收敛到缓慢移动的长期相关性矩阵，该矩阵根据一种或多种灵活的傅立叶形式演化。该模型应用于两个数据集：美国股票投资组合；以及美国股票、债券、黄金和石油投资组合。1 至 60 天范围内的模型拟合和样本外预测支持所提出的方法。