Joint Dynamics For The Underlying Asset and Its Implied Volatility Surface: A New Methodology For Option Risk Management

François, Pascal; Galarneau-Vincent, Rémi; Gauthier, Geneviève; Godin, Frédéric

doi:10.2139/ssrn.4319972

Cited by 3 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A straightforward extension is to explore how well the hedging strategies work for other stochastic volatility processes for the underlying asset and for processes where there are jumps in the price of the underlying asset. Alternatively, a joint process for the asset price and volatility surface, such as that proposed by Francois et al (2023), can be investigated. The analysis should incorporate the way delta, gamma, and vega are normally calculated by practitioners (i.e., by substituting the implied volatility into the Black-Scholes-Merton formulas).…”

Section: Conclusion and Further Studymentioning

confidence: 99%

Gamma and vega hedging using deep distributional reinforcement learning

Cao

Chen²,

Farghadani³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

We show how reinforcement learning can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta-neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.

show abstract

Section: Conclusion and Further Studymentioning

confidence: 99%

Gamma and vega hedging using deep distributional reinforcement learning

Cao

Chen²,

Farghadani³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…They also propose a second approach based on FPCA of the changes of the log IV surfaces and model the coefficients by Ornstein-Uhlenbeck processes, identical to what was done in , but now including a down-sampling method for reducing static arbitrage. Francois et al (2023) model the joint dynamics of the IV surfaces and its underlying using a five-factor parametric representation of the IV surfaces. The underlying returns and the factor coefficients are modeled using GARCH-type models, with a Gaussian copula capturing the dependence structure.…”

Section: Introductionmentioning

confidence: 99%