A deep learning dynamic programming scheme prices path-dependent convertible bonds under GBM, CEV and Heston dynamics, showing that reset and call clauses dominate the underlying process in determining value and that downward resets can paradoxically lower bond prices.
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In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.
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A deep learning approach for pricing convertible bonds with path-dependent reset and call provisions
A deep learning dynamic programming scheme prices path-dependent convertible bonds under GBM, CEV and Heston dynamics, showing that reset and call clauses dominate the underlying process in determining value and that downward resets can paradoxically lower bond prices.
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Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution
In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.