Yaxing Yang
Biographic Data
| ID | 8920154 |
|---|---|
| NAME | Yaxing Yang |
| GIVEN NAMES | Yaxing |
| FAMILY NAME | Yang |
| SIGNATURE | YANG Y |
| AFFILIATIONS | Xiamen University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Testing Serial Correlation and ARCH Effect of High-Dimensional Time-Series Data
This article proposes several tests for detecting serial correlation and ARCH effect in high-dimensional data. The dimension of data p=p(n) may go to infinity when the sample size n→∞. It is shown that the sample autocorrelations and the sample rank autocorrelations (Spearman’s rank correlation) of the L1-norm of data are asymptotically normal. Two portmanteau tests based, respectively, on the norm and its rank are shown to be asymptotically χ2-d…
Inference for Heavy-Tailed and Multiple-Threshold Double Autoregressive Models
This article develops a systematic inference procedure for heavy-tailed and multiple-threshold double autoregressive (MTDAR) models. We first study its quasi-maximum exponential likelihood estimator (QMELE). It is shown that the estimated thresholds are n-consistent, each of which converges weakly to the smallest minimizer of a two-sided compound Poisson process. The remaining parameters are n-consistent and asymptotically normal. Based on this t…
No prominent works on this page.
Inference for Heavy-Tailed and Multiple-Threshold Double Autoregressive Models
This article develops a systematic inference procedure for heavy-tailed and multiple-threshold double autoregressive (MTDAR) models. We first study its quasi-maximum exponential likelihood estimator (QMELE). It is shown that the estimated thresholds are n-consistent, each of which converges weakly to the smallest minimizer of a two-sided compound Poisson process. The remaining parameters are n-consistent and asymptotically normal. Based on this t…
Testing Serial Correlation and ARCH Effect of High-Dimensional Time-Series Data
This article proposes several tests for detecting serial correlation and ARCH effect in high-dimensional data. The dimension of data p=p(n) may go to infinity when the sample size n→∞. It is shown that the sample autocorrelations and the sample rank autocorrelations (Spearman’s rank correlation) of the L1-norm of data are asymptotically normal. Two portmanteau tests based, respectively, on the norm and its rank are shown to be asymptotically χ2-d…
Applied Mathematics (2 works) · Financial Risk and Volatility Modeling (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Artificial Intelligence (1 works) · Autocorrelation (1 works) · Autoregressive model (1 works) · Combinatorics (1 works) · Complex Systems and Time Series Analysis (1 works)