Option Hedging with Risk Averse Reinforcement Learning
- URL: http://arxiv.org/abs/2010.12245v1
- Date: Fri, 23 Oct 2020 09:08:24 GMT
- Title: Option Hedging with Risk Averse Reinforcement Learning
- Authors: Edoardo Vittori, Michele Trapletti, Marcello Restelli
- Abstract summary: We show how risk-averse reinforcement learning can be used to hedge options.
We apply a state-of-the-art risk-averse algorithm to a vanilla option hedging environment.
- Score: 34.85783251852863
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we show how risk-averse reinforcement learning can be used to
hedge options. We apply a state-of-the-art risk-averse 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 volatility-p\&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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