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

In: New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Giovanni De Luca

    (University of Naples Parthenope)

  • Anna Pia Di Iorio

    (University of Naples Parthenope)

  • Andrea Montanino

    (University of Naples Parthenope)

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.

Suggested Citation

  • Giovanni De Luca & Anna Pia Di Iorio & Andrea Montanino, 2025. "Backtesting Expected Shortfall for Bitcoin: A Joint Combined LSTM-Based Approach," Springer Books, in: Michele La Rocca & Massimiliano Menzietti & Cira Perna & Marilena Sibillo (ed.), New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 120-131, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-05551-4_11
    DOI: 10.1007/978-3-032-05551-4_11
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