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
收藏
页码：13 / 18
页数：6
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