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| 001 | 9.877442 |
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| 003 | CaOODSP |
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| 005 | 20221107164957 |
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| 006 | m go d f |
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| 007 | cr |n||||||||| |
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| 008 | 190802t20192019oncd #ob f000 0 eng d |
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| 040 | |aCaOODSP|beng|erda|cCaOODSP |
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| 043 | |an-cn--- |
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| 086 | 1 |aFB3-5/2019-28E-PDF |
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| 100 | 1 |aDanielsson, Jon, |eauthor. |
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| 245 | 10|aTail index estimation : |bquantile-driven threshold selection / |cby Jon Danielsson, Lerby M. Ergun, Laurens de Haan and Casper G. de Vries. |
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| 264 | 1|aOttawa : |bBank of Canada = Banque du Canada, |c2019. |
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| 264 | 4|c©2019 |
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| 300 | |a1 online resource (ii, 47 pages) : |bcharts. |
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| 336 | |atext|btxt|2rdacontent |
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| 337 | |acomputer|bc|2rdamedia |
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| 338 | |aonline resource|bcr|2rdacarrier |
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| 490 | 1 |aBank of Canada staff working paper, |x1701-9397 ; |v2019-28 |
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| 500 | |a"August 2019." |
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| 504 | |aIncludes bibliographical references (page 27-29). |
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| 520 | |a"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. |
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| 692 | 07|2gccst|aEconomic statistics |
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| 700 | 1 |aErgun, Lerby M., |eauthor. |
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| 700 | 1 |ade Haan, Laurens, |eauthor. |
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| 700 | 1 |aDe Vries, Casper G., |eauthor. |
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| 710 | 2 |aBank of Canada. |
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| 830 | #0|aStaff working paper (Bank of Canada)|x1701-9397 ; |v2019-28.|w(CaOODSP)9.806221 |
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| 856 | 40|qPDF|s1.84 MB|uhttps://publications.gc.ca/collections/collection_2019/banque-bank-canada/FB3-5-2019-28-eng.pdf |
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