When ratios fall: A dynamic approach to contingent convertibles

Battle Count: 80

Highly relevant for fixed income and credit derivatives trading desks dealing with CoCo bonds and AT1 instruments. The model provides improved pricing accuracy (~30% RMSE reduction) and hedging performance (~50% error reduction) compared to existing approaches. Directly applicable to delta-gamma hedging of CoCo positions, risk management of bank capital instruments, and early warning signal detection. However, the complexity of implementation and the specialized nature of CoCo markets limit broader applicability to general quantitative trading strategies.

Very high complexity: requires implementing compound Poisson-Erlang density functions with confluent hypergeometric functions, parabolic cylinder functions, running supremum distributions via Takács-type formulas, time-inhomogeneous Poisson intensity models with squared Brownian motion components, Girsanov measure changes for jump-diffusion processes, multi-dimensional numerical integration (Gauss-Kronrod quadrature), RBF interpolation for computational speedup, MLE estimation with sign corrections for shifted distributions, and multi-parameter calibration (up to 12 parameters). The paper provides detailed formulas but implementation requires significant numerical expertise.

The paper provides detailed implementation steps (Section 5), parameter categorization (Table 1), and mentions Python code and processed data available from authors upon reasonable request. However, no public repository is linked. The model involves complex numerical procedures (Gauss-Kronrod quadrature, RBF interpolation, multi-parameter calibration) that require significant implementation effort. Five case studies with specific data sources (WRDS CRSP, Bloomberg) are described.