6 citations · 6 across the 3 of their papers we have counts for
5 papers
Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks
Yize Wang, Qianqian Zhu, Guodong Li
To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformati…
Quantile autoregressive conditional heteroscedasticity
Qianqian Zhu, Songhua Tan, Yao Zheng +1
This paper proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(\infty) form of the GARCH model. Th…
Asymmetric linear double autoregression
Songhua Tan, Qianqian Zhu
This paper proposes the asymmetric linear double autoregression, which jointly models the conditional mean and conditional heteroscedasticity characterized by asymmetric effects. A…
Quantile double autoregression
Qianqian Zhu, Guodong Li
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 meanwhil…
Hybrid Quantile Regression Estimation for Time Series Models with Conditional Heteroscedasticity
Yao Zheng, Qianqian Zhu, Guodong Li +1
Estimating conditional quantiles of financial time series is essential for risk management and many other applications in finance. It is well-known that financial time series displ…