论文标题
光谱定位估计$λ$ -Garch型号
Spectral Targeting Estimation of $λ$-GARCH models
论文作者
论文摘要
本文介绍了正交GARCH模型的新估计量，该模型将靶向估计的（特征值和 - 矢量）与逐步（单变量）估计结合在一起。我们将其表示为光谱靶向估计器。在有限的二阶矩时，此两步估计器在有限的第四阶矩中保持一致，而渐近级别则保持。该估计量特别适合对较大的投资组合进行建模：我们将光谱靶向估计量与准五个资产投资组合的频谱靶向估计量与准最大似然估计量的经验性能。光谱靶向估计量在计算复杂性方面占主导地位，估计值的估计速度高57倍，而两个估计量都会产生相似的样本外预测，这表明光谱靶向估计器非常适合高维经验应用。
This paper presents a novel estimator of orthogonal GARCH models, which combines (eigenvalue and -vector) targeting estimation with stepwise (univariate) estimation. We denote this the spectral targeting estimator. This two-step estimator is consistent under finite second order moments, while asymptotic normality holds under finite fourth order moments. The estimator is especially well suited for modelling larger portfolios: we compare the empirical performance of the spectral targeting estimator to that of the quasi maximum likelihood estimator for five portfolios of 25 assets. The spectral targeting estimator dominates in terms of computational complexity, being up to 57 times faster in estimation, while both estimators produce similar out-of-sample forecasts, indicating that the spectral targeting estimator is well suited for high-dimensional empirical applications.
