多元已实现GARCH模型
A Multivariate Realized GARCH Model
AI总结：
本文提出一种纳入已实现波动率与相关性测度的多元GARCH模型，通过对条件相关矩阵进行无约束向量参数化并引入因子模型，缓解高维建模难题，在九资产收益率数据上相比多个基准表现出更好的样本内外性能。
摘要：
我们提出了一类新颖的多元GARCH模型，该模型纳入了波动率和相关性的已实现测度。关键创新在于对条件相关矩阵采用无约束向量参数化，这使得能够对相关性使用因子模型。该方法巧妙地解决了高维情形下多元GARCH模型面临的主要挑战。作为示例，我们研究了分块相关矩阵，它们自然地简化为条件相关性的线性因子模型。该模型被应用于九项资产的收益率，其样本内和样本外表现在与若干流行基准的比较中表现良好。
英文摘要：
We propose a novel class of multivariate GARCH models that incorporate realized measures of volatility and correlations. The key innovation is an unconstrained vector parametrization of the conditional correlation matrix, which enables the use of factor models for correlations. This approach elegantly addresses the main challenge faced by multivariate GARCH models in high-dimensional settings. As an illustration, we explore block correlation matrices that naturally simplify to linear factor models for the conditional correlations. The model is applied to the returns of nine assets, and its in-sample and out-of-sample performance compares favorably against several popular benchmarks.
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