
Deep Hedging: Learning RiskNeutral Implied Volatility Dynamics
We present a numerically efficient approach for learning a riskneutral ...
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Deep Hedging of Derivatives Using Reinforcement Learning
This paper shows how reinforcement learning can be used to derive optima...
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Learning Agents in BlackScholes Financial Markets: Consensus Dynamics and Volatility Smiles
BlackScholes (BS) is the standard mathematical model for option pricing...
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Implied volatility surface predictability: the case of commodity markets
Recent literature seek to forecast implied volatility derived from equit...
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Valuing Exotic Options and Estimating Model Risk
A common approach to valuing exotic options involves choosing a model an...
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Portfolio risk allocation through Shapley value
We argue that using the Shapley value of cooperative game theory as the ...
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The QLBS QLearner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios
The QLBS model is a discretetime option hedging and pricing model that ...
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Option Hedging with Risk Averse Reinforcement Learning
In this paper we show how riskaverse reinforcement learning can be used to hedge options. We apply a stateoftheart riskaverse algorithm: Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment, considering realistic factors such as discrete time and transaction costs. Realism makes the problem twofold: the agent must both minimize volatility and contain transaction costs, these tasks usually being in competition. We use the algorithm to train a sheaf of agents each characterized by a different risk aversion, so to be able to span an efficient frontier on the volatilityp&l space. The results show that the derived hedging strategy not only outperforms the Black & Scholes delta hedge, but is also extremely robust and flexible, as it can efficiently hedge options with different characteristics and work on markets with different behaviors than what was used in training.
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