CAT Bond Pricing with Kolmogorov–Arnold Networks

Battle Count: 71

CAT bonds are insurance-linked securities traded in capital markets. The paper's fast surrogate pricing model is relevant for risk management desks, insurance-linked securities (ILS) fund managers, and structured product desks. However, it is more directly relevant to insurance risk transfer and catastrophe bond issuance/pricing than to high-frequency or algorithmic trading strategies. The interpretability aspect is valuable for regulatory and compliance contexts in ILS markets.

The pipeline involves multiple stages: Monte Carlo simulation with importance sampling, closed-form baseline computation, KAN training with B-splines, TPE hyperparameter search (100 evaluations), symbolic extraction with pruning/grid refinement/symbolic locking, and final evaluation. Requires familiarity with pykan library, hyperopt, and financial mathematics (compound Poisson processes, Vasicek model). The theoretical components (monotonicity proofs, convergence guarantees) add conceptual complexity but are not required for implementation.

The paper specifies random seed 42, Python libraries (numpy, pandas, PyTorch, pykan, hyperopt, scikit-learn), full hyperparameter ranges, and detailed algorithmic pipeline (Algorithm 1). However, no GitHub repository or code link is provided. The dataset is simulated (100,000 Monte Carlo observations) with specified parameter ranges, making it reproducible in principle but requiring implementation of the full pipeline.