90 citations · 100 across the 17 of their papers we have counts for
17 papers
Estimation for conditional moment models based on martingale difference divergence
Kunyang Song, Feiyu Jiang, Ke Zhu
We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD).Our MDD-based estimation method is formed in the framework of a cont…
Matrix GARCH Model: Inference and Application
Cheng Yu, Dong Li, Feiyu Jiang +1
Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in eco…
Quantiled conditional variance, skewness, and kurtosis by Cornish-Fisher expansion
Ningning Zhang, Ke Zhu
The conditional variance, skewness, and kurtosis play a central role in time series analysis. These three conditional moments (CMs) are often studied by some parametric models but…
Big portfolio selection by graph-based conditional moments method
Zhoufan Zhu, Ningning Zhang, Ke Zhu
How to do big portfolio selection is very important but challenging for both researchers and practitioners. In this paper, we propose a new graph-based conditional moments (GRACE)…
Statistical inference for the logarithmic spatial heteroskedasticity model with exogenous variables
Bing Su, Fukang Zhu, Ke Zhu
The spatial dependence in mean has been well studied by plenty of models in a large strand of literature, however, the investigation of spatial dependence in variance is lagging si…
Pair-switching rerandomization
Ke Zhu, Hanzhong Liu
Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejectio…
Confidence intervals for parameters in high-dimensional sparse vector autoregression
Ke Zhu, Hanzhong Liu
Vector autoregression (VAR) models are widely used to analyze the interrelationship between multiple variables over time. Estimation and inference for the transition matrices of VA…
Testing error distribution by kernelized Stein discrepancy in multivariate time series models
Donghang Luo, Ke Zhu, Huan Gong +1
Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are…
Multi-frequency-band tests for white noise under heteroskedasticity
Mengya Liu, Fukan Zhu, Ke Zhu
This paper proposes a new family of multi-frequency-band (MFB) tests for the white noise hypothesis by using the maximum overlap discrete wavelet packet transform (MODWPT). The MOD…
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…
Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
Feiyu Jiang, Dong Li, Ke Zhu
This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run compone…
Non-standard inference for augmented double autoregressive models with null volatility coefficients
Feiyu Jiang, Dong Li, Ke Zhu
This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Sin…
Time series models for realized covariance matrices based on the matrix-F distribution
Jiayuan Zhou, Feiyu Jiang, Ke Zhu +1
We propose a new Conditional BEKK matrix-F (CBF) model for the time-varying realized covariance (RCOV) matrices. This CBF model is capable of capturing heavy-tailed RCOV, which is…
New HSIC-based tests for independence between two stationary multivariate time series
Guochang Wang, Wai Keung Li, Ke Zhu
This paper proposes some novel one-sided omnibus tests for independence between two multivariate stationary time series. These new tests apply the Hilbert-Schmidt independence crit…
Statistical inference for autoregressive models under heteroscedasticity of unknown form
Ke Zhu
This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the…
A Double AR Model Without Intercept: an Alternative to Modeling Nonstationarity and Heteroscedasticity
Dong Li, Shaojun Guo, Ke Zhu
This paper presents a double AR model without intercept (DARWIN model) and provides us a new way to study the non-stationary heteroskedastic time series. It is shown that the DARWI…
Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA--GARCH/IGARCH models
Ke Zhu, Shiqing Ling
This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA--GARCH models. Under only a fractional moment condition, the s…