Yanxi Hou
Biographic Data
| ID | 8920971 |
|---|---|
| NAME | Yanxi Hou |
| GIVEN NAMES | Yanxi |
| FAMILY NAME | Hou |
| SIGNATURE | HOU Y |
| AFFILIATIONS | Georgia Institute of Technology |
| ORCID | 0000-0002-3065-8255 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes
Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful way to quantify the conditional effect of some predictors or risk factors on an interesting response variable. However, when it comes to the estimation of extremal conditional expectile, the traditional inferen…
Statistical Inference for a Relative Risk Measure
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is established when the observations are independent. To effectively construct an interval without complicated asymptotic variance estimation, a jackknife empirical likelihood inference procedure based on the smoothed …
No prominent works on this page.
Statistical Inference for a Relative Risk Measure
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is established when the observations are independent. To effectively construct an interval without complicated asymptotic variance estimation, a jackknife empirical likelihood inference procedure based on the smoothed …
Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes
Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful way to quantify the conditional effect of some predictors or risk factors on an interesting response variable. However, when it comes to the estimation of extremal conditional expectile, the traditional inferen…
Artificial Intelligence (2 works) · Computer Science (2 works) · Econometrics (2 works) · Financial Risk and Volatility Modeling (2 works) · Inference (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Asymptotic distribution (1 work) · Confidence interval (1 work)