This article proposes omnibus portmanteau tests for contrasting adequacy of time series models. The test statistics are based on combining the autocorrelation function of the conditional residuals, the autocorrelation function of the conditional squared residuals, and the cross-correlation function between these residuals and their squares. The maximum likelihood estimator is used to derive the asymptotic distribution of the proposed test statistics under a general class of time series models, including ARMA, GARCH, and other nonlinear structures. An extensive Monte Carlo simulation study shows that the proposed tests successfully control the type I error probability and tend to have more power than other competitor tests in many scenarios. Two applications to a set of weekly stock returns for 92 companies from the S&P 500 demonstrate the practical use of the proposed tests.

翻译：本条提议对时间序列模型的适足性进行总括端点测试。 测试统计数据的基础是将有条件残余物的自动关系功能、有条件的平方残余物的自动关系功能和这些残余物及其正方体之间的交叉关系功能结合起来。 最大可能性估计值用于在一般时间序列模型类别下得出拟议测试统计数据的无症状分布, 包括ARMA、 GARCHH 和其他非线性结构。 内容广泛的蒙特卡洛模拟研究表明,拟议的测试成功地控制了I型误差概率,并且在许多情景中往往比其他竞争者测试更有力量。 S & P 500 中92家公司的一套每周股票收益的两种应用显示了拟议测试的实际用途。