Andrew J. Patton, Yasin Simsek
arXiv 30 May 2023 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 5 citations (OpenAlex)
arXiv:2305.18991 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Breiman, L (2001) Random forests | 1.000 | 5 | 4 | 100% |
| 2 | Harvey, A. C (2013) Dynamic Models for Volatility and Heavy Tails, with Applications to Financial and Economic Time Series, volume 52 | 1.000 | 5 | 4 | 100% |
| 3 | Breiman, L., Friedman, J., Stone, C. J., and Olshen, R (1984) Classification and Regression Trees | 1.000 | 5 | 3 | 100% |
| 4 | Creal, D., Koopman, S. J., and Lucas, A (2013) Generalized autoregressive score models with applications | 0.969 | 11 | 6 | 91% |
| 5 | Audrino, F. and Bühlmann, P (2001) Tree-structured generalized autoregressive conditional heteroscedastic models | 0.928 | 4 | 3 | 100% |
| 6 | Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H (2009) The Elements of Statistical Learning: Data mining, Inference, and Prediction, volume 2 | 0.928 | 4 | 3 | 100% |
| 7 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.874 | 5 | 2 | 100% |
| 8 | Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J (2017) Classification and regression trees | 0.843 | 3 | 3 | 100% |
| 9 | Creal, D., Koopman, S. J., and Lucas, A (2011) A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations | 0.737 | 4 | 3 | 50% |
| 10 | Engle, R. F. and Russell, J. R (1998) Autoregressive conditional duration: a new model for irregularly spaced transaction data | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching | 0.405 | 1 | 1 |