Backtesting Expected Shortfall for Bitcoin: A Joint Combined LSTM-Based Approach

Giovanni De Luca, Anna Pia Di Iorio () and Andrea Montanino
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Giovanni De Luca: University of Naples Parthenope
Anna Pia Di Iorio: University of Naples Parthenope
Andrea Montanino: University of Naples Parthenope

A chapter in New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2025, pp 120-131 from Springer

Abstract: Abstract This work aims to identify the most accurate model in passing the joint-combined backtesting procedure for Value-at-Risk and Expected Shortfall forecasts for Bitcoin. First, GARCH and Markov Switching GARCH are estimated and used to forecast the corresponding VaR and ES. Next, the Long Short-Term Memory model is applied to refine these risk measures. Finally, four models (GARCH, Markov-Switching GARCH, Joint-Combined, Long-Short Term Memory Joint-Combined) are compared based on average loss and backtesting performances. Results suggest that the LSTM-Joint-Combined model apparently represents the best model delivering the lowest average predictive loss across the evaluated settings. Furthermore, it considerably enhances the efficacy of the JC approach.

Keywords: Backtesting; Cryptocurrencies; Expected Shortfall; Joint-Combined Regression; LSTM; Markov-Switching GARCH (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-05551-4_11

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DOI: 10.1007/978-3-032-05551-4_11

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