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

被引：0

作者：

Andreani, Mila ^{[1
]}

Candila, Vincenzo ^{[2
]}

Petrella, Lea ^{[2
]}

机构：

[1] Scuola Normale Super Pisa, Pisa, Italy

[2] Sapienza Univ Rome, MEMOTEF Depart, Rome, Italy

来源：

MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE, MAF 2022 | 2022年

关键词：

Value-at-risk; Quantile regression; Random Forests; Mixed data sampling;

D O I：

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

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：13 / 18

页数：6

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