1 code implementation • 8 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.
no code implementations • 18 Jan 2022 • Bekzhan Kerimkulov, James-Michael Leahy, David Šiška, Lukasz Szpruch
We show that the objective function is increasing along the gradient flow.
1 code implementation • 8 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.
no code implementations • 10 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.
no code implementations • 11 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.
no code implementations • 20 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.