In high-frequency market making, action robustness (Sinkhorn regularization) dominates uncertainty tolerance in reshaping sequential quoting and inventory, while excessive robustness can cut execution opportunities in illiquid names.
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Under intermediate volatility and limited samples, model-free DDPG finds dynamic-arbitrage strategies more reliably than SLSQP run on noisily estimated Almgren-Chriss impact parameters, even though the latter knows the true functional form.
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Robustness in Sequential Decision Making under Evolving Uncertainty: Evidence from High-Frequency Market Making
In high-frequency market making, action robustness (Sinkhorn regularization) dominates uncertainty tolerance in reshaping sequential quoting and inventory, while excessive robustness can cut execution opportunities in illiquid names.
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Can Reinforcement Learning Efficiently Discover Price Manipulation?
Under intermediate volatility and limited samples, model-free DDPG finds dynamic-arbitrage strategies more reliably than SLSQP run on noisily estimated Almgren-Chriss impact parameters, even though the latter knows the true functional form.