No shortfall of ES estimators: Insights from cryptocurrency portfolios

Matúš Horváth and Tomáš Výrost

Finance Research Letters, 2025, vol. 73, issue C

Abstract: Since the Basel III accords, Expected Shortfall (ES) has become the recommended tail-risk measure in financial investments. Several methods of different theoretical backgrounds, complexity, and ease of implementation have since been developed for ES. As the competing set of models for ES grows, the question of which one to use becomes relevant to both academia and practitioners. We compare the predictive ability of four classes of models for ES estimation and identify a superior set. We verify the viability of these models in portfolio applications based on cryptocurrencies, an asset class with high volatility, particularly suitable for tail risk mitigation.

Keywords: Value-at-risk; Expected shortfall; Forecasting; Cryptocurrency; Portfolio (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612324017148
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:finlet:v:73:y:2025:i:c:s1544612324017148

DOI: 10.1016/j.frl.2024.106685

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

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