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

Mila Andreani (), Vincenzo Candila () and Lea Petrella ()
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Mila Andreani: Scuola Normale Superiore
Vincenzo Candila: Sapienza University of Rome, MEMOTEF Depart.
Lea Petrella: Sapienza University of Rome, MEMOTEF Depart.

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 13-18 from Springer

Abstract: 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.

Keywords: Value-at-risk; Quantile regression; Random Forests; Mixed data sampling (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_3

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DOI: 10.1007/978-3-030-99638-3_3

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