Capturing information in extreme events

Omid Ardakani

Economics Letters, 2023, vol. 231, issue C

Abstract: This study integrates information theory and extreme value theory to enhance the prediction of extreme events. Information-theoretic measures provide a foundation for model comparison in tails. The theoretical findings suggest that (1) the entropy of block maxima converges to the entropy of the generalized extreme value distribution, (2) the rate of convergence is controlled by its shape parameter, and (3) the entropy of block maxima is a monotonically decreasing function of the block size. Empirical analysis of E-mini S&P, 500 futures data evaluates the financial risk, capturing information content of extreme events using entropy and Kullback–Leibler divergence.

Keywords: Entropy; Generalized extreme value distribution; Generalized Pareto; Kullback–Leibler divergence; Tail risk (search for similar items in EconPapers)
JEL-codes: C13 C51 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176523003269
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:231:y:2023:i:c:s0165176523003269

DOI: 10.1016/j.econlet.2023.111301

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().