A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging

Fathi, Ali; Hientzsch, Bernhard

doi:10.2139/ssrn.4360692

Cited by 2 publications

(6 citation statements)

References 33 publications

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“…Ref. [60] compares the performance of DDPG and an NN-approximated DP solution to the optimal hedging strategy. Using GBM simulated data, Ref.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

“…Ref. [60] compares the performance of DDPG to the NN approximation of a model-based DP strategy. Ref.…”

Section: Rl Methodsmentioning

confidence: 99%

“…As described in Section 1, the BS Vega, BS Gamma and BS Delta are all BS Greeks, and the BS Greeks are all functions of the BS option price and time [3]. In addition to [49,52], the state spaces of [51,59,60] also include the BS Delta in the state space in their studies. Several papers explicitly explain that the BS Delta can be deduced from the option and stock prices, and its inclusion in the state space only serves to unnecessarily augment the input size [1,31,32].…”

Section: State and Action Spacesmentioning

confidence: 99%

“…Ref. [60] performs a comparison of DRL agents trained with BS Delta and BS Delta-free state spaces to show that the inclusion of the BS Delta leads to better learning in the dynamic hedging environment.…”

Section: State and Action Spacesmentioning

confidence: 99%

“…Refs. [49,52,56,60] include the option price in the state vector. Option price inclusion is notable in [49], which also includes volatility, the BS Delta, the stock price and the number of shares.…”

Section: State and Action Spacesmentioning

confidence: 99%

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Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Pickard,

Lawryshyn

2023

Mathematics

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This paper reviews 17 studies addressing dynamic option hedging in frictional markets through Deep Reinforcement Learning (DRL). Specifically, this work analyzes the DRL models, state and action spaces, reward formulations, data generation processes and results for each study. It is found that policy methods such as DDPG are more commonly employed due to their suitability for continuous action spaces. Despite diverse state space definitions, a lack of consensus exists on variable inclusion, prompting a call for thorough sensitivity analyses. Mean-variance metrics prevail in reward formulations, with episodic return, VaR and CvaR also yielding comparable results. Geometric Brownian motion is the primary data generation process, supplemented by stochastic volatility models like SABR (stochastic alpha, beta, rho) and the Heston model. RL agents, particularly those monitoring transaction costs, consistently outperform the Black–Scholes Delta method in frictional environments. Although consistent results emerge under constant and stochastic volatility scenarios, variations arise when employing real data. The lack of a standardized testing dataset or universal benchmark in the RL hedging space makes it difficult to compare results across different studies. A recommended future direction for this work is an implementation of DRL for hedging American options and an investigation of how DRL performs compared to other numerical American option hedging methods.

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“…Ref. [60] compares the performance of DDPG and an NN-approximated DP solution to the optimal hedging strategy. Using GBM simulated data, Ref.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

“…Ref. [60] compares the performance of DDPG to the NN approximation of a model-based DP strategy. Ref.…”

Section: Rl Methodsmentioning

confidence: 99%