Goodness‐of‐fit tests for the multivariate Student‐tdistribution based on i.i.d. data, and for GARCH observations
提出一种基于经验特征函数的多元学生t分布拟合优度检验方法，适用于独立同分布数据和GARCH模型中的创新分布，具有线性变换不变性和全局一致性，并通过蒙特卡洛模拟和金融数据验证。
We consider goodness‐of‐fit tests for the multivariate Student's t ‐distribution with i.i.d. data and for the innovation distribution in a generalized autoregressive conditional heteroskedasticity model. The methods are based on the empirical characteristic function and are relatively easy to implement, invariant under linear transformations, and globally consistent. Asymptotic properties of the proposed procedures are investigated, while the finite‐sample properties are illustrated by means of a Monte Carlo study. The procedures are also applied to real data from the financial markets.