00000nam 2200000zi 4500 9.877442 CaOODSP 20221107164957 m go d f cr |n||||||||| 190802t20192019oncd #ob f000 0 eng d CaOODSP eng rda CaOODSP n-cn--- FB3-5/2019-28E-PDF Danielsson, Jon, author. Tail index estimation : quantile-driven threshold selection / by Jon Danielsson, Lerby M. Ergun, Laurens de Haan and Casper G. de Vries. Ottawa : Bank of Canada = Banque du Canada, 2019. ©2019 1 online resource (ii, 47 pages) : charts. text txt rdacontent computer c rdamedia online resource cr rdacarrier Bank of Canada staff working paper, 1701-9397 ; 2019-28 "August 2019." Includes bibliographical references (page 27-29). "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"--Abstract. gccst Economic statistics Ergun, Lerby M., author. de Haan, Laurens, author. De Vries, Casper G., author. Bank of Canada. Staff working paper (Bank of Canada) 1701-9397 ; 2019-28. (CaOODSP)9.806221 PDF 1.84 MB https://publications.gc.ca/collections/collection_2019/banque-bank-canada/FB3-5-2019-28-eng.pdf