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Conditional autoregressive value-at-risk: all flavours of CAViaR

Author

Listed:
  • Pedro Henrique Melo Albuquerque
  • Matheus Facure Alves
  • Maísa Cardoso Aniceto
  • Gustavo Monteiro Pereira

Abstract

In this article, we studied 13 parametric CAViaR models for 27 stock's indices concerning the bias-variance dilemma, providing an empirical golden rule for choosing the CAViaR structure over unknown information distributional features of financial data. Our findings pointed out that the adaptive model should be chosen when no prior information is available since it presented the smallest MSE in 23 of 27 assets. Furthermore, we also noted that in most cases, the CAViaR models overestimate the validation value-at-risk. This might not be troublesome from a regulators' point of view, since firms and financial institutions that would use those models will likely overestimate risk and hence adopt more conservative politics. However, from the firm's point of view, this means that they will likely operate in a suboptimal risk regime which.

Suggested Citation

  • Pedro Henrique Melo Albuquerque & Matheus Facure Alves & Maísa Cardoso Aniceto & Gustavo Monteiro Pereira, 2020. "Conditional autoregressive value-at-risk: all flavours of CAViaR," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 6(3), pages 238-254.
    • Handle: RePEc:ids:ijbfmi:v:6:y:2020:i:3:p:238-254
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