A framework that applies a risk measure on a distribution of models, approximated by subsampling, robustifies financial optimization against model uncertainty and scales via a memory-efficient CVaR-SGD algorithm.
Deep Bellman Hedging
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abstract
We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The key characteristics of our approach are: the ability to hedge with derivatives such as forwards, swaps, futures, options; incorporation of trading frictions such as trading cost and liquidity constraints; applicability for any reasonable portfolio of financial instruments; realistic, continuous state and action spaces; and formal risk-adjusted return objectives. Most importantly, the trained model provides an optimal hedge for arbitrary initial portfolios and market states without the need for re-training. We also prove existence of finite solutions to our Bellman equation, and show the relation to our vanilla Deep Hedging approach
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Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
A framework that applies a risk measure on a distribution of models, approximated by subsampling, robustifies financial optimization against model uncertainty and scales via a memory-efficient CVaR-SGD algorithm.