Lookback option pricing under the double Heston model using a deep learning algorithm

To price floating strike lookback options, we obtain a partial differential equation (PDE) according to the double Heston model. To solve the PDE, we employ a deep learning algorithm called the deep Galerkin method (DGM), which is well-suited for high-dimensional PDEs. Finally, we compare the obtain...

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Veröffentlicht in:	Computational & applied mathematics 2022-12, Vol.41 (8), Article 378
Hauptverfasser:	Motameni, Mahsa, Mehrdoust, Farshid, Najafi, Ali Reza
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Sprache:	eng
Schlagworte:	Algorithms Applications of Mathematics Applied physics Computational mathematics Computational Mathematics and Numerical Analysis Deep learning Galerkin method Machine learning Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Mathematics Mathematics and Statistics Partial differential equations
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description	To price floating strike lookback options, we obtain a partial differential equation (PDE) according to the double Heston model. To solve the PDE, we employ a deep learning algorithm called the deep Galerkin method (DGM), which is well-suited for high-dimensional PDEs. Finally, we compare the obtained results from mentioned method with the option price under the Monte Carlo simulation method.
doi_str_mv	10.1007/s40314-022-02098-5
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subjects	Algorithms Applications of Mathematics Applied physics Computational mathematics Computational Mathematics and Numerical Analysis Deep learning Galerkin method Machine learning Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Mathematics Mathematics and Statistics Partial differential equations
title	Lookback option pricing under the double Heston model using a deep learning algorithm
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