Forecasting Value-at-Risk using deep neural network quantile regression

Ilias Chronopoulos, Aristeidis Raftapostolos and George Kapetanios

Essex Finance Centre Working Papers from University of Essex, Essex Business School

Abstract: In this paper we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast Value-at-Risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This paper also contributes to the interpretability of neural networks output by making comparisons between the commonly used SHAP values and an alternative method based on partial derivatives.

Keywords: Quantile regression; machine learning; neural networks; value-at-risk; forecasting (search for similar items in EconPapers)
Date: 2023-02-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for and nep-rmg
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Citations: View citations in EconPapers (5)

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Journal Article: Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression* (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:34837

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