QUANTILE DOUBLE AUTOREGRESSION
针对金融时间序列在不同分位数水平上结构不同且存在条件异方差的问题，提出分位数双重自回归模型，推导了严格平稳性，并构建了自加权条件分位数估计和三个端口检验，通过模拟和S&P500指数分析验证了模型的有效性。
Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and 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, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.