Statistical Inference for a Relative Risk Measure
Bibliographic Data
| ID | 19418994 |
|---|---|
| Authors | Yi He (0000-0002-5562-4190, Monash University), Yanxi Hou (0000-0002-3065-8255, Georgia Institute of Technology), Liang Peng (0000-0003-3801-4849, Georgia State University), Jiliang Sheng (0000-0002-2890-4090, Jiangxi University of Finance and Economics) |
| Year | 2019 |
| Volume | 37 |
| Issue | 2 |
| Pages | 301-311 |
| Publication date | 2019-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2017.1321549 |
| OpenAlex | W2608010173 |
| Language | EN |
| References cited | 32 |
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 nonparametric estimation is provided with a Wilks type of result in case of independent observations. When data follow from AR-GARCH models, the relative risk measure with respect to the errors becomes useful and so we propose a corresponding nonparametric estimator. A simulation study and real-life data analysis show that the proposed relative risk measure is useful in monitoring systemic risk
Asymptotic distribution · Confidence interval · Data mining · Delta method · Econometrics · Economics · Estimator · Inference · Interval estimation · Jackknife resampling · Nonparametric statistics · Point estimation · Risk measure · Statistical inference · Statistics · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Statistical Methods and Inference · Artificial Intelligence
| Highly cited | No |
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