40 citations · 71 across the 13 of their papers we have counts for
13 papers
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…
HAR-Ito models and high-dimensional HAR modeling for high-frequency data
Huiling Yuan, Kexin Lu, Yifeng Guo +1
It is an important task to model realized volatilities for high-frequency data in finance and economics and, as arguably the most popular model, the heterogeneous autoregressive (H…
Cross-Layer Retrospective Retrieving via Layer Attention
Yanwen Fang, Yuxi Cai, Jintai Chen +3
More and more evidence has shown that strengthening layer interactions can enhance the representation power of a deep neural network, while self-attention excels at learning interd…
High-Frequency-Based Volatility Model with Network Structure
Huiling Yuan, Guodong Li, Junhui Wang
This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The mode…
High-Dimensional Vector Autoregression with Common Response and Predictor Factors
Di Wang, Xiaoyu Zhang, Guodong Li +1
The reduced-rank vector autoregressive (VAR) model can be interpreted as a supervised factor model, where two factor modelings are simultaneously applied to response and predictor…
Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models
Xiaoyu Zhang, Di Wang, Heng Lian +1
Many panel data have the latent subgroup effect on individuals, and it is important to correctly identify these groups since the efficiency of resulting estimators can be improved…
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…
Do RNN and LSTM have Long Memory?
Jingyu Zhao, Feiqing Huang, Jia Lv +4
The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in…
Hybrid quantile estimation for asymmetric power GARCH models
Guochang Wang, Ke Zhu, Guodong Li +1
Asymmetric power GARCH models have been widely used to study the higher order moments of financial returns, while their quantile estimation has been rarely investigated. This paper…
Compact Autoregressive Network
Di Wang, Feiqing Huang, Jingyu Zhao +2
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, t…
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…
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…