Wasserstein Robust Market Making via Entropy Regularization
By: Zhou Fang, Arie Israel
Potential Business Impact:
Makes trading stocks fairer and more predictable.
In this paper, we introduce a robust market making framework based on Wasserstein distance, utilizing a stochastic policy approach enhanced by entropy regularization. We demonstrate that, under mild assumptions, the robust market making problem can be reformulated as a convex optimization question. Additionally, we outline a methodology for selecting the optimal radius of the Wasserstein ball, further refining our framework's effectiveness.
Similar Papers
Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
Optimization and Control
Helps computers learn fair decisions even with uncertain information.
Exactly or Approximately Wasserstein Distributionally Robust Estimation According to Wasserstein Radii Being Small or Large
Signal Processing
Makes computer guesses more accurate with noisy data.
Robust distortion risk measures with linear penalty under distribution uncertainty
Risk Management
Makes money predictions safer with uncertain numbers.