From the 1 of 7 linked papers with an AI index.
7 papers
Is Deep Hedging Reinforcement Learning?
Frédéric Godin
The paper argues that the deep hedging framework, which trains neural network policies via Monte‑Carlo policy‑gradient methods to minimize risk measures, should be classified as re…
Learning to Hedge Swaptions
Zaniar Ahmadi, Frédéric Godin
This paper investigates the deep hedging framework, based on reinforcement learning (RL), for the dynamic hedging of swaptions, contrasting its performance with traditional sensiti…
Deep Hedging with Options Using the Implied Volatility Surface
Pascal François, Geneviève Gauthier, Frédéric Godin +1
We propose a deep hedging framework for index option portfolios, grounded in a realistic market simulator that captures the joint dynamics of S&P 500 returns and the full implied v…
Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
Pascal François, Geneviève Gauthier, Frédéric Godin +1
We present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The opti…
Deep Reinforcement Learning Algorithms for Option Hedging
Andrei Neagu, Frédéric Godin, Leila Kosseim
Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep R…
Survival Multiarmed Bandits with Bootstrapping Methods
Peter Veroutis, Frédéric Godin
The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open prob…