标题： 分位数双自回归 显示英文标题

标题： Quantile double autoregression

摘要： 许多金融时间序列在不同的分位数水平上具有不同的结构，并且同时表现出条件异方差现象。与此同时，仍然缺乏一种时间序列模型来同时容纳上述两种特征。本文通过提出一种新的条件异方差模型——称为分位数双自回归模型来填补这一空白。该新模型的严格平稳性被推导出来，并建议使用自加权条件分位数估计。展示了原始双自回归模型的两个有希望的特性得以保留。基于分位数自相关函数和自加权概念，构建了两个组合测试，可以用来检查拟合条件分位数的充分性。通过模拟研究检验了所提出的推理工具的有限样本性能，并通过分析标准普尔500指数进一步证明了新模型的必要性。 显示英文摘要

摘要： Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroscedasticity at the same time. In the meanwhile, it is still lack of a time series model to accommodate both of the above features simultaneously. This paper fills the gap by proposing a novel conditional heteroscedastic model, which is called the quantile double autoregression. The strict stationarity of the new model is derived, and a self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, two portmanteau tests are constructed, and they can be used in conjunction to check the adequacy of fitted conditional quantiles. The finite-sample performance of the proposed inference tools is examined by simulation studies, and the necessity of the new model is further demonstrated by analyzing the S&P500 Index.