Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Atashbar, Tohid; Shi, Rui Aruhan

doi:10.5089/9798400224713.001

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Other works that take a comprehensive look at RL in finance include [35,36]. In addition to these broader surveys, narrower reviews have examined the use of RL applications in specific financial subdomains, such as algorithmic trading [37,38], market making [39], economics [40,41] and portfolio optimization [42].…”

Section: Similar Workmentioning

confidence: 99%

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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Section: Similar Workmentioning

confidence: 99%

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Pickard,

Lawryshyn

2023

Mathematics

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Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling

Brusatin,

Padoan,

Coletta

et al. 2024

Proceedings of the 5th ACM International Conference on AI in Finance

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Taming the curse of dimensionality: quantitative economics with deep learning

Fernández-Villaverde,

Nuño,

Perla

2024

Documentos De Trabajo

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We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a review of the applications of neural networks in quantitative economics and provide arguments for cautious optimism.

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Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Cited by 3 publications

References 35 publications

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review

Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling

Taming the curse of dimensionality: quantitative economics with deep learning

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