Exploiting tail shape biases to discriminate between stable and student t alternatives.

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
17 Downloads (Pure)

Abstract

The nonnormal stable laws and Student t distributions are used to model the unconditional distribution of financial asset returns, as both models display heavy tails. The relevance of the two models is subject to debate because empirical estimates of the tail shape conditional on either model give conflicting signals. This stems from opposing bias terms. We exploit the biases to discriminate between the two distributions. A sign estimator for the second-order scale parameter strengthens our results. Tail estimates based on asset return data match the bias induced by finite-variance unconditional Student t data and the generalized autoregressive conditional heteroscedasticity process.
Original languageEnglish
Pages (from-to)708-726
Number of pages19
JournalJournal of Applied Econometrics
Volume33
Issue number5
DOIs
Publication statusPublished - 2018

Fingerprint

Dive into the research topics of 'Exploiting tail shape biases to discriminate between stable and student t alternatives.'. Together they form a unique fingerprint.

Cite this