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

Pedro Henrique Melo Albuquerque, Matheus Facure Alves, Maísa Cardoso Aniceto and Gustavo Monteiro Pereira

International Journal of Business Forecasting and Marketing Intelligence, 2020, vol. 6, issue 3, 238-254

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.

Keywords: value-at-risk; VaR; conditional autoregressive value-at-risk; CAViaR; bias-variance dilemma; risk. (search for similar items in EconPapers)
Date: 2020
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