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- W3213873037 abstract "We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e.g. for a spot price and options written on the same underlying. We extend our results to markets with frictions, in which case we find near-martingale measures under which the prices of hedging instruments are martingales within their bid/ask spread. By removing the drift, we are then able to learn using Deep Hedging a clean hedge for an exotic payoff which is not polluted by the trading strategy trying to make money from statistical arbitrage opportunities. We correspondingly highlight the robustness of this hedge vs estimation error of the original market simulator. We discuss applications to two market simulators." @default.
- W3213873037 created "2021-11-22" @default.
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- W3213873037 date "2021-11-15" @default.
- W3213873037 modified "2023-09-24" @default.
- W3213873037 title "Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures" @default.
- W3213873037 doi "https://doi.org/10.48550/arxiv.2111.07844" @default.
- W3213873037 hasPublicationYear "2021" @default.
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