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Found 3 papers, 0 papers with code
Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook.
Deeper Hedging: A New Agent-based Model for Effective Deep Hedging
With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels.
Graph-informed simulation-based inference for models of active matter
Moreover, we demonstrate that a small number (from one to three) snapshots of the system can be used for parameter inference and that this graph-informed approach outperforms typical metrics such as the average velocity or mean square displacement of the system.
