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.

翻译：本文提出通过决策树和随机森林对广义自回归得分（GAS）模型（Creal等人，2013；Harvey，2013）的参数进行局部化处理，以提升其预测性能的方法。这些方法避免了基于核方法所面临的维度灾难，并允许同时利用多个状态变量的信息。我们将新模型应用于四项不同的实证分析，在所有应用中，所提出的新方法均显著优于基准GAS模型。在股票收益波动率与密度预测的应用中，最优GAS树模型揭示了杠杆效应和方差风险溢价效应。股票-债券相关性研究显示，最优GAS森林预测中存在避险效应证据，而对高频交易持续时间的分析则发现了成交量-波动率效应。