Time series quantile regression using random forests

TL;DR

This paper extends Generalized Random Forests to time series quantile regression, proving estimator consistency and demonstrating improved risk sensitivity in financial data analysis.

Contribution

It introduces a novel theoretical framework for time series quantile regression using GRF, establishing consistency under general conditions and applying it to real stock data.

Findings

Estimator is consistent for time series data.

The method outperforms others in volatility sensitivity.

Effective in real stock market risk estimation.

Abstract

We discuss an application of Generalized Random Forests (GRF) proposed by Athey et al.(2019) to quantile regression for time series data. We extracted the theoretical results of the GRF consistency for i.i.d. data to time series data. In particular, in the main theorem, based only on the general assumptions for time series data in Davis and Nielsen (2020), and trees in Athey et al.(2019), we show that the tsQRF (time series Quantile Regression Forests) estimator is consistent. Davis and Nielsen (2020) also discussed the estimation problem using Random Forests (RF) for time series data, but the construction procedure of the RF treated by the GRF is essentially different, and different ideas are used throughout the theoretical proof. In addition, a simulation and real data analysis were conducted.In the simulation, the accuracy of the conditional quantile estimation was evaluated under time series models. In the real data using the Nikkei Stock Average, our estimator is demonstrated to be more sensitive than the others in terms of volatility, thus preventing underestimation of risk.