Misspecification Testing for the Conditional Distribution Model in GARCH-Type Processes
研究了GARCH类模型中创新分布拟合优度的检验方法，采用函数自助法进行推断，并通过蒙特卡洛实验和外汇收益率数据评估了检验效果。
In this article, we study goodness of fit tests for some distributions of the innovations which are usually adopted to explain the behavior of financial time series. Inference is developed in the context of GARCH-type models. Functional bootstrap tests are employed, assuming that the conditional means and variances of the model are correctly specified. The performances of the functional tests are assessed with a Monte Carlo experiment, based on some of the most common distributions adopted in the financial framework. The results of an application to the series of squared residuals from a PARCH(1,1) model fitted to a series of foreign exchange rates returns are also shown.