# Generalized Autoregressive Score Trees and Forests

arXiv: https://arxiv.org/abs/2305.18991 · HTML: https://mlsanity.com/paper/2305.18991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 9 of 9)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terrain](https://mlsanity.com/paper/1804.02921/) (2018)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [The Macroeconomy as a Random Forest](https://mlsanity.com/paper/2006.12724/) (2020)
- [Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs](https://mlsanity.com/paper/2008.12706/) (2020)
- [A Statistical Recurrent Stochastic Volatility Model for Stock Markets](https://mlsanity.com/paper/1906.02884/) (2019)
- [Recurrent Conditional Heteroskedasticity](https://mlsanity.com/paper/2010.13061/) (2020)
- [HARNet: A Convolutional Neural Network for Realized Volatility Forecasting](https://mlsanity.com/paper/2205.07719/) (2022)

## Abstract (arXiv)

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal et. al, 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods significantly outperform the baseline GAS model. In our applications to stock return volatility and density prediction, the optimal GAS tree model reveals a leverage effect and a variance risk premium effect. Our study of stock-bond dependence finds evidence of a flight-to-quality effect in the optimal GAS forest forecasts, while our analysis of high-frequency trade durations uncovers a volume-volatility effect.