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Generalized Autoregressive Score Trees and Forests

Author

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  • Andrew J. Patton
  • Yasin Simsek
Abstract
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.

Suggested Citation

  • Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
  • Handle: RePEc:arx:papers:2305.18991
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    File URL: http://arxiv.org/pdf/2305.18991
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    References listed on IDEAS

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    Cited by:

    1. Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching," Papers 2408.12863, arXiv.org.
    2. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.

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