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Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data

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Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2022)

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Abstract

In this paper we introduce the use of mixed-frequency variables in a quantile regression framework to compute high-frequency conditional quantiles by means of low-frequency variables. We merge the well-known Quantile Regression Forest algorithm and the recently proposed Mixed-Data-Sampling model to build a comprehensive methodology to jointly model complexity, non-linearity and mixed-frequencies. Due to the link between quantile and the Value-at-Risk (VaR) measure, we compare our novel methodology with the most popular ones in VaR forecasting.

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Authors and Affiliations

  1. Scuola Normale Superiore, Pisa, Italy

    Mila Andreani

  2. MEMOTEF Depart., Sapienza University of Rome, Rome, Italy

    Vincenzo Candila & Lea Petrella

Authors
  1. Mila Andreani
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  2. Vincenzo Candila
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  3. Lea Petrella
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Corresponding author

Correspondence to Mila Andreani .

Editor information

Editors and Affiliations

  1. Department of Economics, Ca' Foscari University of Venice, Venice, Italy

    Marco Corazza

  2. Department of Economics and Statistics, University of Salerno, Fisciano, Salerno, Italy

    Cira Perna

  3. Department of Economics, Ca' Foscari University of Venice, Venice, Italy

    Claudio Pizzi

  4. Department of Economics and Statistics, University of Salerno, Fisciano, Italy

    Marilena Sibillo

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Andreani, M., Candila, V., Petrella, L. (2022). Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. Springer, Cham. https://doi.org/10.1007/978-3-030-99638-3_3

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