Further processing options
Tail index estimation: quantile-driven threshold selection
Saved in:
| Authors and Corporations: | , , , |
|---|---|
| Other Authors: | Ergun, Lerby M. [Author] • Haan, Laurens de 1937- [Author] • Vries, Casper G. de 1955- [Author] |
| Type of Resource: | E-Book |
| Language: | English |
| published: | |
| Series: |
Bank of Canada: Staff working paper ; 2019, 28 (August 2019)
|
| Subjects: | |
| Source: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| Summary: | The selection of upper order statistics in tail estimation is notoriously difficult. Methods that are based on asymptotic arguments, like minimizing the asymptotic MSE, do not perform well in finite samples. Here, we advance a data-driven method that minimizes the maximum distance between the fitted Pareto type tail and the observed quantile. To analyze the finite sample properties of the metric, we perform rigorous simulation studies. In most cases, the finite sample-based methods perform best. To demonstrate the economic relevance of choosing the proper methodology, we use daily equity return data from the CRSP database and find economically relevant variation between the tail index estimates. |
|---|---|
| Physical Description: | 1 Online-Ressource (circa 50 Seiten); Illustrationen |