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Equilibria and incentives for illiquid auction markets
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We study a toy two-player game for periodic double auction markets to generate liquidity. The game has imperfect information, which allows us to link market spreads with signal strength. We characterize Nash equilibria in cases with or without incentives from the exchange. This enables us to derive new insights about price formation and incentives design. We show in particular that without any incentives, the market is inefficient and does not lead to any trade between market participants. We however prove that quadratic fees indexed on each players half spread leads to a transaction and we propose a quantitative value for the optimal fees that the exchange has to propose in this model to generate liquidity.
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Cited by 1 Pith paper
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Learning Market Making with Closing Auctions
A neural-fitted Q-learning market maker that anticipates the closing auction beats Avellaneda-Stoikov and TWAP benchmarks on mean returns in the paper's simulations.
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