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

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Mila Andreani

    (Scuola Normale Superiore)

  • Vincenzo Candila

    (Sapienza University of Rome, MEMOTEF Depart.)

  • Lea Petrella

    (Sapienza University of Rome, MEMOTEF Depart.)

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.

Suggested Citation

  • Mila Andreani & Vincenzo Candila & Lea Petrella, 2022. "Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 13-18, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_3
    DOI: 10.1007/978-3-030-99638-3_3
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