No shortfall of ES estimators: Insights from cryptocurrency portfolios

Expected Shortfall forecasting models for predicting cryptocurrency tail risk.

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Horváth, Matúš (2025). No shortfall of ES estimators: Insights from cryptocurrency portfolios. Q2 ecosystem. Available at https://quantinar.com/course/1000/no-shortfall-of-es-estimators:-insights-from-cryptocurrency-portfolios or http://dx.doi.org/10.24818/IDA-CL/2025.144.

@misc{horvth2025, author = {Horváth, Matúš}, title = {No shortfall of ES estimators: Insights from cryptocurrency portfolios}, year = {2025}, howpublished = {Q2 ecosystem}, doi = {http://dx.doi.org/10.24818/IDA-CL/2025.144.}, url = {https://quantinar.com/course/1000/no-shortfall-of-es-estimators:-insights-from-cryptocurrency-portfolios} }
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	No shortfall of ES estimators: Insights from cryptocurrency portfolios (pdf)	1.21 M

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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.