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

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  • Chronopoulos, Ilias
  • Raftapostolos, Aristeidis
  • Kapetanios, George

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.

Suggested Citation

  • Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    • Handle: RePEc:esy:uefcwp:34837
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    File URL: https://repository.essex.ac.uk/34837/
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    Cited by:

    1. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.

    More about this item

    Keywords

    Quantile regression; machine learning; neural networks; value-at-risk; forecasting;
    All these keywords.

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