• Media type: E-Book
  • Title: Deep hedging: hedging derivatives under generic market frictions using reinforcement learning
  • Contributor: Buehler, Hans [Author]; Gonon, Lukas [Author]; Teichmann, Josef [Author]; Wood, Ben [Author]; Mohan, Baranidharan [Author]; Kochems, Jonathan [Author]
  • Published: Geneva: Swiss Finance Institute, 2019
  • Published in: Swiss Finance Institute: Research paper series ; 2019,80
  • Extent: 1 Online-Ressource (circa 14 Seiten); Illustrationen
  • Language: English
  • DOI: 10.2139/ssrn.3355706
  • Identifier:
  • Keywords: Reinforcement Learning ; Imperfect Hedging ; Derivatives Pricing ; Derivatives Hedging ; Deep Learning ; Graue Literatur
  • Origination:
  • Footnote:
  • Description: This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work "https://www.ssrn.com/abstract=3120710" https://www.ssrn.com/abstract=3120710, here using notation more common in the machine learning literature.The objective is to maximize a non-linear risk-adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model-independent algorithm which can be used for such problems at scale
  • Access State: Open Access