Option market making with hedging-induced market impact

Battle Count: 72

Highly relevant to quantitative trading, particularly for option market makers and derivatives desks. The paper directly addresses the practical problem of hedging-induced market impact in option market making, provides a rigorous no-arbitrage framework, and demonstrates via neural policy optimization how impact-aware strategies significantly outperform naive delta-hedging benchmarks, especially under inventory imbalances and low liquidity. The coupling between option order flow and underlying price impact is a key practical concern for any derivatives market maker.

High complexity due to: (1) mixed continuous-impulsive control formulation, (2) coupled option-underlying dynamics with Hawkes/Cox processes, (3) general order book representation with generalized inverses, (4) differentiable discrete-time simulator requiring careful handling of non-differentiabilities, (5) neural policy training with soft-clipping for constraint enforcement, (6) multiple interacting state variables (price, spread, resilience, intensities, inventories). The theoretical well-posedness proofs add further complexity for rigorous implementation.

The paper provides detailed parameter tables (Tables 1-2), explicit model equations, and describes the neural network architecture and training procedure. However, no code repository is mentioned, and the Cox intensity functions and reference price construction require careful implementation. The simulator and training pipeline are described at a high level but lack pseudocode or hyperparameter details beyond learning rate and batch size.