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Forecasting Financial Risk Using Quantile Random Forests
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AI
DOI：10.1002/for.70146.png)
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Abstract
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This paper introduces a financial risk forecasting model that effectively exploits information from a large set of economic and financial predictor variables. The model is built using generalized quantile random forests, a nonparametric machine learning method that naturally permits variable interactions and nonlinear relationships. We use a model-free variable screening technique and a robust cross-validation approach to minimize the risk of overfitting. Our risk model produces competitive value-at-risk and expected shortfall forecasts at both 1-day-ahead and 10-day-ahead horizons. A dynamic portfolio insurance strategy that uses the VaR and ES forecasts from our risk model generates attractive Sharpe, Sortino, and Omega ratios, particularly at the 10-day forecast horizon. Additionally, we provide a detailed analysis of the dynamic importance of our predictor variables. Our findings demonstrate the utility of combining large datasets with tree-based algorithms for financial risk forecasting.
Keywords:
expected shortfall
machine learning
random forests
value at risk
variable screening
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