10 citations · 29 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
High-Dimensional Low-Rank Tensor Autoregressive Time Series Modeling
Di Wang, Yao Zheng, Guodong Li
Modern technological advances have enabled an unprecedented amount of structured data with complex temporal dependence, urging the need for new methods to efficiently model and for…
High-dimensional vector autoregressive time series modeling via tensor decomposition
Di Wang, Yao Zheng, Heng Lian +1
The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and l…
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…