Reinforcement Learning Simulation Metrics: QQ plots, ACF graphs, and Volatility Analysis by@reinforcement
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Reinforcement Learning Simulation Metrics: QQ plots, ACF graphs, and Volatility Analysis
by Reinforcement Technology AdvancementsJanuary 1st, 2025
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This section provides extended analysis of RL simulations, showcasing QQ plots, ACF graphs, and volatility clustering. Key metrics like kurtosis and inventory risk are compared across groups (continual training, testing, and untrained), offering deeper insights into market dynamics and agent behavior.
This is Part 10 of a 11-part series based on the research paper “Reinforcement Learning In Agent-based Market Simulation: Unveiling Realistic Stylized Facts And Behavior”. Use the table of links below to navigate to the next part.
Table of Links
Part 1: Abstract & Introduction
Part 4: Agents & Simulation Details
Part 8: Market and Agent Responsiveness to External Events
Part 9: Conclusion & References
Part 10: Additional Simulation Results
Part 11: Simulation Configuration
7 Appendix
7.1 Additional Simulation Results
Figure 1 is the QQ plot for simulations’ prices(10 seconds) generated with all five groups of setup, providing additional insights to Figure 3a. More details in section 5.1.
Figure 2 shows the ACF graphs for the price returns from groups of testing and untrained. More details in section 5.1.
Figure 3 shows the ACF graphs for the absolute price returns from groups of testing and untrained. More details in section 5.1.
Figure 4 shows the volatility clustering analysis graphs for groups of testing and untrained. The analysis method can be found in [1]. More details in section 5.1.
Table 1 provides additional market characteristics (Kurtosis and Inventory Risk) for groups of continual train, testing, and non train.
Authors:
(1) Zhiyuan Yao, Stevens Institute of Technology, Hoboken, New Jersey, USA ([email protected]);
(2) Zheng Li, Stevens Institute of Technology, Hoboken, New Jersey, USA ([email protected]);
(3) Matthew Thomas, Stevens Institute of Technology, Hoboken, New Jersey, USA ([email protected]);
(4) Ionut Florescu, Stevens Institute of Technology, Hoboken, New Jersey, USA ([email protected]).
This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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