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The Journal of Financial Data Science

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Dynamic Replication and Hedging: A Reinforcement Learning Approach

Petter N. Kolm and Gordon Ritter
The Journal of Financial Data Science Winter 2019, 1 (1) 159-171; DOI: https://doi.org/10.3905/jfds.2019.1.1.159
Petter N. Kolm
is a clinical professor and director of the Mathematics in Finance Master’s Program at NYU’s Courant Institute of Mathematical Sciences in New York, NY
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Gordon Ritter
is adjunct professor at NYU’s Courant Institute of Mathematical Sciences, NYU’s Tandon School of Engineering, and the Department of Mathematics of Baruch College, and is a Professor of Practice in Rutgers University’s Department of Statistics in New York, NY
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Abstract

The authors of this article address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model. Based on reinforcement learning (RL), a well-established machine learning technique, the authors propose a model that is flexible, accurate and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All that it needs is a good simulator, in which transaction costs and options prices are simulated accurately.

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The Journal of Financial Data Science: 1 (1)
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Dynamic Replication and Hedging: A Reinforcement Learning Approach
Petter N. Kolm, Gordon Ritter
The Journal of Financial Data Science Jan 2019, 1 (1) 159-171; DOI: 10.3905/jfds.2019.1.1.159

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Dynamic Replication and Hedging: A Reinforcement Learning Approach
Petter N. Kolm, Gordon Ritter
The Journal of Financial Data Science Jan 2019, 1 (1) 159-171; DOI: 10.3905/jfds.2019.1.1.159
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  • Article
    • Abstract
    • REINFORCEMENT LEARNING
    • TRAINING VIA SIMULATION AND BATCH LEARNING
    • AUTOMATIC HEDGING IN THEORY
    • AUTOMATIC HEDGING IN PRACTICE
    • CONCLUSIONS
    • ENDNOTES
    • REFERENCES
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