论文标题
不对称线性双重自动赛
Asymmetric linear double autoregression
论文作者
论文摘要
本文提出了不对称的线性双重自动追踪，该循环均具有不对称效应为特征的条件平均值和条件异质性。为存在严格固定的解决方案建立了足够的条件。提出了贝叶斯信息标准（BIC）及其修改版本的贝叶斯信息标准（BIC），因此提出了用于模型选择的贝叶斯信息标准（BIC）。为了检测波动率的不对称效应，提出了WALD，LAGRANGE乘数和准类比率测试统计数据，并在NULL和局部替代假设下建立了它们的限制分布。此外，构建了混合的Portmanteau测试，以检查拟合模型的充分性。在数据过程中建立了所有QMLE，BICS，非对称测试和混合Portmanteau测试的推理工具的渐近特性，包括数据过程，这使得新模型及其推理工具适用于重型数据。仿真研究表明，所提出的方法在有限样本中表现良好，而对S \＆P500指数的经验应用说明了新模型的有用性。
This paper proposes the asymmetric linear double autoregression, which jointly models the conditional mean and conditional heteroscedasticity characterized by asymmetric effects. A sufficient condition is established for the existence of a strictly stationary solution. With a quasi-maximum likelihood estimation (QMLE) procedure introduced, a Bayesian information criterion (BIC) and its modified version are proposed for model selection. To detect asymmetric effects in the volatility, the Wald, Lagrange multiplier and quasi-likelihood ratio test statistics are put forward, and their limiting distributions are established under both null and local alternative hypotheses. Moreover, a mixed portmanteau test is constructed to check the adequacy of the fitted model. All asymptotic properties of inference tools including QMLE, BICs, asymmetric tests and the mixed portmanteau test, are established without any moment condition on the data process, which makes the new model and its inference tools applicable for heavy-tailed data. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical application to S\&P500 Index illustrates the usefulness of the new model.
