Search Results for author: David Šiška

Found 6 papers, 2 papers with code

Inefficiency of CFMs: hedging perspective and agent-based simulations

1 code implementation8 Feb 2023 samuel cohen, Marc Sabaté Vidales, David Šiška, Łukasz Szpruch

We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk.

Robust pricing and hedging via neural SDEs

1 code implementation8 Jul 2020 Patryk Gierjatowicz, Marc Sabate-Vidales, David Šiška, Lukasz Szpruch, Žan Žurič

Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data.

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Gradient Flows for Regularized Stochastic Control Problems

no code implementations10 Jun 2020 David Šiška, Łukasz Szpruch

This paper studies stochastic control problems with the action space taken to be probability measures, with the objective penalised by the relative entropy.

Mean-Field Neural ODEs via Relaxed Optimal Control

no code implementations11 Dec 2019 Jean-François Jabir, David Šiška, Łukasz Szpruch

We develop a framework for the analysis of deep neural networks and neural ODE models that are trained with stochastic gradient algorithms.

Uniform error estimates for artificial neural network approximations for heat equations

no code implementations20 Nov 2019 Lukas Gonon, Philipp Grohs, Arnulf Jentzen, David Kofler, David Šiška

These mathematical results from the scientific literature prove in part that algorithms based on ANNs are capable of overcoming the curse of dimensionality in the numerical approximation of high-dimensional PDEs.

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