Pricing Catastrophe Bonds — A Probabilistic Machine Learning Approach
By Xiaowei Chen, Hong Li, Yufan Lu, Rui Zhou
Rating
1534
Battle Count: 87
Relevance
7/10
While focused on catastrophe bonds, the methodology could be adapted for other financial instruments and risk modeling in quantitative trading
Implementation Complexity
6/10
Requires implementation of XGBoost and Conformal Prediction, as well as feature engineering and model interpretation techniques
Reproducibility
4/5
The paper provides detailed methodology and data sources, but the exact dataset is not publicly available
About this paper
Methodology: XGBoost with Conformal Prediction. Problem types: Regression, Time Series Forecasting.
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