Dynamic Replication and Hedging : A Reinforcement Learning Approach

We 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, a well-established machine learning technique, our model is shown to be flexible, accurate and very promising for real-world applications

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Year of publication:	[2021]
Authors:	Kolm, Petter N. ; Ritter, Gordon
Publisher:	[S.l.] : SSRN
Subject:	Hedging \| Lernprozess \| Learning process \| Theorie \| Theory \| Lernen \| Learning
Description of contents:	Abstract [papers.ssrn.com] ; Abstract [doi.org]

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Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Kolm, Petter N. and Gordon Ritter. "Dynamic Replication and Hedging: A Reinforcement Learning Approach." The Journal of Financial Data Science 1.1 (2019) Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 11, 2018 erstellt Volltext nicht verfügbar
Other identifiers:	10.2139/ssrn.3281235 [DOI]
Classification:	G11 - Portfolio Choice ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013240054