�ݺ�ߣshows by User: ChihebBenHammouda1

�ݺ�ߣshows by User: ChihebBenHammouda1 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ChihebBenHammouda1 / Sun, 07 Apr 2024 09:04:04 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ChihebBenHammouda1 Efficient Fourier Pricing of Multi-Asset Options: Quasi-Monte Carlo & Domain Transformation Approach /slideshow/efficient-fourier-pricing-of-multiasset-options-quasimonte-carlo-domain-transformation-approach/267146790 presentation-240407090404-967a69e5
My talk at ICCF24 with abstract: Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. While the Monte Carlo (MC) method remains a prevalent choice, its slow convergence rate can impede practical applications. Fourier methods, leveraging the knowledge of the characteristic function, have shown promise in valuing single-asset options but face hurdles in the high-dimensional context. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over quadrature methods in the Fourier domain, and over the MC method in the physical domain for options with up to 15 assets.]]>
My talk at ICCF24 with abstract: Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. While the Monte Carlo (MC) method remains a prevalent choice, its slow convergence rate can impede practical applications. Fourier methods, leveraging the knowledge of the characteristic function, have shown promise in valuing single-asset options but face hurdles in the high-dimensional context. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over quadrature methods in the Fourier domain, and over the MC method in the physical domain for options with up to 15 assets.]]> Sun, 07 Apr 2024 09:04:04 GMT /slideshow/efficient-fourier-pricing-of-multiasset-options-quasimonte-carlo-domain-transformation-approach/267146790 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Efficient Fourier Pricing of Multi-Asset Options: Quasi-Monte Carlo & Domain Transformation Approach ChihebBenHammouda1 My talk at ICCF24 with abstract: Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. While the Monte Carlo (MC) method remains a prevalent choice, its slow convergence rate can impede practical applications. Fourier methods, leveraging the knowledge of the characteristic function, have shown promise in valuing single-asset options but face hurdles in the high-dimensional context. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over quadrature methods in the Fourier domain, and over the MC method in the physical domain for options with up to 15 assets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-240407090404-967a69e5-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk at ICCF24 with abstract: Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. While the Monte Carlo (MC) method remains a prevalent choice, its slow convergence rate can impede practical applications. Fourier methods, leveraging the knowledge of the characteristic function, have shown promise in valuing single-asset options but face hurdles in the high-dimensional context. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over quadrature methods in the Fourier domain, and over the MC method in the physical domain for options with up to 15 assets.

Efficient Fourier Pricing of Multi-Asset Options: Quasi-Monte Carlo & Domain Transformation Approach from Chiheb Ben Hammouda

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0 Leiden_VU_Delft_seminar short.pdf /slideshow/leidenvudelftseminar-shortpdf/263494313 leidenvudelftseminarshort-231116125931-568c07b8
talk titled "Generic Importance Sampling via Optimal Control for Stochastic Reaction Networks" at the joint Leiden/VU/Delft-seminar]]>
talk titled "Generic Importance Sampling via Optimal Control for Stochastic Reaction Networks" at the joint Leiden/VU/Delft-seminar]]> Thu, 16 Nov 2023 12:59:31 GMT /slideshow/leidenvudelftseminar-shortpdf/263494313 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Leiden_VU_Delft_seminar short.pdf ChihebBenHammouda1 talk titled "Generic Importance Sampling via Optimal Control for Stochastic Reaction Networks" at the joint Leiden/VU/Delft-seminar <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/leidenvudelftseminarshort-231116125931-568c07b8-thumbnail.jpg?width=120&height=120&fit=bounds" /> talk titled "Generic Importance Sampling via Optimal Control for Stochastic Reaction Networks" at the joint Leiden/VU/Delft-seminar

Leiden_VU_Delft_seminar short.pdf from Chiheb Ben Hammouda

]]> 57 0 https://cdn.slidesharecdn.com/ss_thumbnails/leidenvudelftseminarshort-231116125931-568c07b8-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Presentation.pdf /slideshow/presentationpdf-258758027/258758027 presentation-230701084501-6ba8736f
My talk at the International Conference on Monte Carlo Methods and Applications (MCM2032) related to advances in mathematical aspects of stochastic simulation and Monte Carlo methods at Sorbonne Université June 28, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).]]>
My talk at the International Conference on Monte Carlo Methods and Applications (MCM2032) related to advances in mathematical aspects of stochastic simulation and Monte Carlo methods at Sorbonne Université June 28, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).]]> Sat, 01 Jul 2023 08:45:01 GMT /slideshow/presentationpdf-258758027/258758027 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Presentation.pdf ChihebBenHammouda1 My talk at the International Conference on Monte Carlo Methods and Applications (MCM2032) related to advances in mathematical aspects of stochastic simulation and Monte Carlo methods at Sorbonne Université June 28, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-230701084501-6ba8736f-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk at the International Conference on Monte Carlo Methods and Applications (MCM2032) related to advances in mathematical aspects of stochastic simulation and Monte Carlo methods at Sorbonne Université June 28, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).

Presentation.pdf from Chiheb Ben Hammouda

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0 Presentation.pdf /slideshow/presentationpdf-258308330/258308330 presentation-230608105528-f1b84d26
My talk at SIAM FM 23]]>
My talk at SIAM FM 23]]> Thu, 08 Jun 2023 10:55:28 GMT /slideshow/presentationpdf-258308330/258308330 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Presentation.pdf ChihebBenHammouda1 My talk at SIAM FM 23 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-230608105528-f1b84d26-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk at SIAM FM 23

Presentation.pdf from Chiheb Ben Hammouda

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0 KAUST_talk_short.pdf /slideshow/kausttalkshortpdf/258162238 kausttalkshort-230531154446-ae89ff73
Stochastic reaction networks (SRNs) are a particular class of continuous-time Markov chains used to model a wide range of phenomena, including biological/chemical reactions, epidemics, risk theory, queuing, and supply chain/social/multi-agents networks. In this context, we explore the efficient estimation of statistical quantities, particularly rare event probabilities, and propose two alternative importance sampling (IS) approaches [1,2] to improve the Monte Carlo (MC) estimator efficiency. The key challenge in the IS framework is to choose an appropriate change of probability measure to achieve substantial variance reduction, which often requires insights into the underlying problem. Therefore, we propose an automated approach to obtain a highly efficient path-dependent measure change based on an original connection between finding optimal IS parameters and solving a variance minimization problem via a stochastic optimal control formulation. We pursue two alternative approaches to mitigate the curse of dimensionality when solving the resulting dynamic programming problem. In the first approach [1], we propose a learning-based method to approximate the value function using a neural network, where the parameters are determined via a stochastic optimization algorithm. As an alternative, we present in [2] a dimension reduction method, based on mapping the problem to a significantly lower dimensional space via the Markovian projection (MP) idea. The output of this model reduction technique is a low dimensional SRN (potentially one dimension) that preserves the marginal distribution of the original high-dimensional SRN system. The dynamics of the projected process are obtained via a discrete $L^2$ regression. By solving a resulting projected Hamilton-Jacobi-Bellman (HJB) equation for the reduced-dimensional SRN, we get projected IS parameters, which are then mapped back to the original full-dimensional SRN system, and result in an efficient IS-MC estimator of the full-dimensional SRN. Our analysis and numerical experiments verify that both proposed IS (learning based and MP-HJB-IS) approaches substantially reduce the MC estimator’s variance, resulting in a lower computational complexity in the rare event regime than standard MC estimators. [1] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. Learning-based importance sampling via stochastic optimal control for stochastic reaction net-works. Statistics and Computing 33, no. 3 (2023): 58. [2] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. (2023). Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach. To appear soon.]]>
Stochastic reaction networks (SRNs) are a particular class of continuous-time Markov chains used to model a wide range of phenomena, including biological/chemical reactions, epidemics, risk theory, queuing, and supply chain/social/multi-agents networks. In this context, we explore the efficient estimation of statistical quantities, particularly rare event probabilities, and propose two alternative importance sampling (IS) approaches [1,2] to improve the Monte Carlo (MC) estimator efficiency. The key challenge in the IS framework is to choose an appropriate change of probability measure to achieve substantial variance reduction, which often requires insights into the underlying problem. Therefore, we propose an automated approach to obtain a highly efficient path-dependent measure change based on an original connection between finding optimal IS parameters and solving a variance minimization problem via a stochastic optimal control formulation. We pursue two alternative approaches to mitigate the curse of dimensionality when solving the resulting dynamic programming problem. In the first approach [1], we propose a learning-based method to approximate the value function using a neural network, where the parameters are determined via a stochastic optimization algorithm. As an alternative, we present in [2] a dimension reduction method, based on mapping the problem to a significantly lower dimensional space via the Markovian projection (MP) idea. The output of this model reduction technique is a low dimensional SRN (potentially one dimension) that preserves the marginal distribution of the original high-dimensional SRN system. The dynamics of the projected process are obtained via a discrete $L^2$ regression. By solving a resulting projected Hamilton-Jacobi-Bellman (HJB) equation for the reduced-dimensional SRN, we get projected IS parameters, which are then mapped back to the original full-dimensional SRN system, and result in an efficient IS-MC estimator of the full-dimensional SRN. Our analysis and numerical experiments verify that both proposed IS (learning based and MP-HJB-IS) approaches substantially reduce the MC estimator’s variance, resulting in a lower computational complexity in the rare event regime than standard MC estimators. [1] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. Learning-based importance sampling via stochastic optimal control for stochastic reaction net-works. Statistics and Computing 33, no. 3 (2023): 58. [2] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. (2023). Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach. To appear soon.]]> Wed, 31 May 2023 15:44:46 GMT /slideshow/kausttalkshortpdf/258162238 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) KAUST_talk_short.pdf ChihebBenHammouda1 Stochastic reaction networks (SRNs) are a particular class of continuous-time Markov chains used to model a wide range of phenomena, including biological/chemical reactions, epidemics, risk theory, queuing, and supply chain/social/multi-agents networks. In this context, we explore the efficient estimation of statistical quantities, particularly rare event probabilities, and propose two alternative importance sampling (IS) approaches [1,2] to improve the Monte Carlo (MC) estimator efficiency. The key challenge in the IS framework is to choose an appropriate change of probability measure to achieve substantial variance reduction, which often requires insights into the underlying problem. Therefore, we propose an automated approach to obtain a highly efficient path-dependent measure change based on an original connection between finding optimal IS parameters and solving a variance minimization problem via a stochastic optimal control formulation. We pursue two alternative approaches to mitigate the curse of dimensionality when solving the resulting dynamic programming problem. In the first approach [1], we propose a learning-based method to approximate the value function using a neural network, where the parameters are determined via a stochastic optimization algorithm. As an alternative, we present in [2] a dimension reduction method, based on mapping the problem to a significantly lower dimensional space via the Markovian projection (MP) idea. The output of this model reduction technique is a low dimensional SRN (potentially one dimension) that preserves the marginal distribution of the original high-dimensional SRN system. The dynamics of the projected process are obtained via a discrete $L^2$ regression. By solving a resulting projected Hamilton-Jacobi-Bellman (HJB) equation for the reduced-dimensional SRN, we get projected IS parameters, which are then mapped back to the original full-dimensional SRN system, and result in an efficient IS-MC estimator of the full-dimensional SRN. Our analysis and numerical experiments verify that both proposed IS (learning based and MP-HJB-IS) approaches substantially reduce the MC estimator’s variance, resulting in a lower computational complexity in the rare event regime than standard MC estimators. [1] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. Learning-based importance sampling via stochastic optimal control for stochastic reaction net-works. Statistics and Computing 33, no. 3 (2023): 58. [2] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. (2023). Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach. To appear soon. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/kausttalkshort-230531154446-ae89ff73-thumbnail.jpg?width=120&height=120&fit=bounds" /> Stochastic reaction networks (SRNs) are a particular class of continuous-time Markov chains used to model a wide range of phenomena, including biological/chemical reactions, epidemics, risk theory, queuing, and supply chain/social/multi-agents networks. In this context, we explore the efficient estimation of statistical quantities, particularly rare event probabilities, and propose two alternative importance sampling (IS) approaches [1,2] to improve the Monte Carlo (MC) estimator efficiency. The key challenge in the IS framework is to choose an appropriate change of probability measure to achieve substantial variance reduction, which often requires insights into the underlying problem. Therefore, we propose an automated approach to obtain a highly efficient path-dependent measure change based on an original connection between finding optimal IS parameters and solving a variance minimization problem via a stochastic optimal control formulation. We pursue two alternative approaches to mitigate the curse of dimensionality when solving the resulting dynamic programming problem. In the first approach [1], we propose a learning-based method to approximate the value function using a neural network, where the parameters are determined via a stochastic optimization algorithm. As an alternative, we present in [2] a dimension reduction method, based on mapping the problem to a significantly lower dimensional space via the Markovian projection (MP) idea. The output of this model reduction technique is a low dimensional SRN (potentially one dimension) that preserves the marginal distribution of the original high-dimensional SRN system. The dynamics of the projected process are obtained via a discrete $L^2$ regression. By solving a resulting projected Hamilton-Jacobi-Bellman (HJB) equation for the reduced-dimensional SRN, we get projected IS parameters, which are then mapped back to the original full-dimensional SRN system, and result in an efficient IS-MC estimator of the full-dimensional SRN. Our analysis and numerical experiments verify that both proposed IS (learning based and MP-HJB-IS) approaches substantially reduce the MC estimator’s variance, resulting in a lower computational complexity in the rare event regime than standard MC estimators. [1] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. Learning-based importance sampling via stochastic optimal control for stochastic reaction net-works. Statistics and Computing 33, no. 3 (2023): 58. [2] Ben Hammouda, C., Ben Rached, N., and Tempone, R., and Wiechert, S. (2023). Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach. To appear soon.

KAUST_talk_short.pdf from Chiheb Ben Hammouda

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0 talk_NASPDE.pdf /slideshow/talknaspdepdf/257882543 talknaspde-230517123421-ac22b054
Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).]]>
Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).]]> Wed, 17 May 2023 12:34:21 GMT /slideshow/talknaspdepdf/257882543 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) talk_NASPDE.pdf ChihebBenHammouda1 Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talknaspde-230517123421-ac22b054-thumbnail.jpg?width=120&height=120&fit=bounds" /> Workshop: Numerical Analysis of Stochastic Partial Differential Equations (NASPDE), in Network Eurandom at Eindhoven University of Technology, May 16, 2023, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://doi.org/10.1080/14697688.2022.2135455), and (ii) "Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities" (link: https://arxiv.org/abs/2003.05708).

talk_NASPDE.pdf from Chiheb Ben Hammouda

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0 Talk_HU_Berlin_Chiheb_benhammouda.pdf /slideshow/talkhuberlinchihebbenhammoudapdf/253868074 talkhuberlinchihebbenhammouda-221029091636-d0dbdfd1
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)]]>
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)]]> Sat, 29 Oct 2022 09:16:35 GMT /slideshow/talkhuberlinchihebbenhammoudapdf/253868074 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Talk_HU_Berlin_Chiheb_benhammouda.pdf ChihebBenHammouda1 My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talkhuberlinchihebbenhammouda-221029091636-d0dbdfd1-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)

Talk_HU_Berlin_Chiheb_benhammouda.pdf from Chiheb Ben Hammouda

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0 MCQMC_talk_Chiheb_Ben_hammouda.pdf /slideshow/mcqmctalkchihebbenhammoudapdf/252279764 mcqmctalkchihebbenhammouda-220722144634-451d6900
My talk at the "15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing " MCQMC conference at Johannes Kepler Universität Linz, July 20, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation."]]>
My talk at the "15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing " MCQMC conference at Johannes Kepler Universität Linz, July 20, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation."]]> Fri, 22 Jul 2022 14:46:33 GMT /slideshow/mcqmctalkchihebbenhammoudapdf/252279764 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) MCQMC_talk_Chiheb_Ben_hammouda.pdf ChihebBenHammouda1 My talk at the "15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing " MCQMC conference at Johannes Kepler Universität Linz, July 20, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mcqmctalkchihebbenhammouda-220722144634-451d6900-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk at the "15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing " MCQMC conference at Johannes Kepler Universität Linz, July 20, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation."

MCQMC_talk_Chiheb_Ben_hammouda.pdf from Chiheb Ben Hammouda

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0 Fourier_Pricing_ICCF_2022.pdf /ChihebBenHammouda1/fourierpricingiccf2022pdf fourierpricingiccf2022-220610142716-602ce25f
Talk of Michael Samet, entitled "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" at the International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022]]>
Talk of Michael Samet, entitled "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" at the International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022]]> Fri, 10 Jun 2022 14:27:16 GMT /ChihebBenHammouda1/fourierpricingiccf2022pdf ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Fourier_Pricing_ICCF_2022.pdf ChihebBenHammouda1 Talk of Michael Samet, entitled "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" at the International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fourierpricingiccf2022-220610142716-602ce25f-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk of Michael Samet, entitled "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" at the International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022

Fourier_Pricing_ICCF_2022.pdf from Chiheb Ben Hammouda

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0 ICCF_2022_talk.pdf /slideshow/iccf2022talkpdf/251961397 iccf2022talk-220610141759-b8edd61c
My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation.]]>
My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation.]]> Fri, 10 Jun 2022 14:17:59 GMT /slideshow/iccf2022talkpdf/251961397 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) ICCF_2022_talk.pdf ChihebBenHammouda1 My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iccf2022talk-220610141759-b8edd61c-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk entitled "Numerical Smoothing and Hierarchical Approximations for Efficient Option Pricing and Density Estimation", that I gave at the "International Conference on Computational Finance (ICCF)", Wuppertal June 6-10, 2022. The talk is related to our recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874) and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708). In these two works, we introduce the numerical smoothing technique that improves the regularity of observables when approximating expectations (or the related integration problems). We provide a smoothness analysis and we show how this technique leads to better performance for the different methods that we used (i) adaptive sparse grids, (ii) Quasi-Monte Carlo, and (iii) multilevel Monte Carlo. Our applications are option pricing and density estimation. Our approach is generic and can be applied to solve a broad class of problems, particularly for approximating distribution functions, financial Greeks computation, and risk estimation.

ICCF_2022_talk.pdf from Chiheb Ben Hammouda

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0 Numerical Smoothing and Hierarchical Approximations for E cient Option Pricing and Density Estimation /slideshow/numerical-smoothing-and-hierarchical-approximations-for-ecient-option-pricing-and-density-estimation/251862801 presentation-220526171957-e61d2447
My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation". ]]>
My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation". ]]> Thu, 26 May 2022 17:19:57 GMT /slideshow/numerical-smoothing-and-hierarchical-approximations-for-ecient-option-pricing-and-density-estimation/251862801 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Numerical Smoothing and Hierarchical Approximations for E cient Option Pricing and Density Estimation ChihebBenHammouda1 My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation". <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-220526171957-e61d2447-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk at the "Stochastic Numerics and Statistical Learning: Theory and Applications" Workshop at KAUST (King Abdullah University of Science and Technology), May 23, 2022, about my recent works "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" and "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation".

Numerical Smoothing and Hierarchical Approximations for E cient Option Pricing and Density Estimation from Chiheb Ben Hammouda

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0 MCQMC 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks /slideshow/mcqmc-2020-talk-importance-sampling-for-a-robust-and-efficient-multilevel-monte-carlo-estimator-for-stochastic-reaction-networks-250003423/250003423 mcmtalklongversion-210818161441
MCM 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks]]>
MCM 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks]]> Wed, 18 Aug 2021 16:14:40 GMT /slideshow/mcqmc-2020-talk-importance-sampling-for-a-robust-and-efficient-multilevel-monte-carlo-estimator-for-stochastic-reaction-networks-250003423/250003423 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) MCQMC 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks ChihebBenHammouda1 MCM 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mcmtalklongversion-210818161441-thumbnail.jpg?width=120&height=120&fit=bounds" /> MCM 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks

MCQMC 2020 talk: Importance Sampling for a Robust and Efficient Multilevel Monte Carlo Estimator for Stochastic Reaction Networks from Chiheb Ben Hammouda

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0 Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model-249012830/249012830 siamfin21talkrbq-210604184825
Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700]]>
Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700]]> Fri, 04 Jun 2021 18:48:25 GMT /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model-249012830/249012830 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model ChihebBenHammouda1 Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/siamfin21talkrbq-210604184825-thumbnail.jpg?width=120&height=120&fit=bounds" /> Conference talk at the SIAM Conference on Financial Mathematics and Engineering, held in virtual format, June 1-4 2021, about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700

Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model from Chiheb Ben Hammouda

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0 Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model-241450998/241450998 presentation-210117042844
Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700]]>
Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700]]> Sun, 17 Jan 2021 04:28:44 GMT /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model-241450998/241450998 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model ChihebBenHammouda1 Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-210117042844-thumbnail.jpg?width=120&height=120&fit=bounds" /> Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700

Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model from Chiheb Ben Hammouda

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0 PhD defense talk slides /ChihebBenHammouda1/phd-defense-talk-slides phddefensetalkslides-200702174630
These are the slides of my PhD entitled "Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks"]]>
These are the slides of my PhD entitled "Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks"]]> Thu, 02 Jul 2020 17:46:30 GMT /ChihebBenHammouda1/phd-defense-talk-slides ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) PhD defense talk slides ChihebBenHammouda1 These are the slides of my PhD entitled "Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks" <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phddefensetalkslides-200702174630-thumbnail.jpg?width=120&height=120&fit=bounds" /> These are the slides of my PhD entitled "Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks"

PhD defense talk slides from Chiheb Ben Hammouda

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0 Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation_seminar_aachen_talk /slideshow/numerical-smoothing-and-hierarchical-approximations-for-efficient-option-pricing-and-density-estimationseminaraachentalk/230833301 numsmoothingseminaraachentalk-200325063323
Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation]]>
Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation]]> Wed, 25 Mar 2020 06:33:23 GMT /slideshow/numerical-smoothing-and-hierarchical-approximations-for-efficient-option-pricing-and-density-estimationseminaraachentalk/230833301 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation_seminar_aachen_talk ChihebBenHammouda1 Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/numsmoothingseminaraachentalk-200325063323-thumbnail.jpg?width=120&height=120&fit=bounds" /> Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation

Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation_seminar_aachen_talk from Chiheb Ben Hammouda

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0 Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model/230222805 roughbergomiseminartalkrwthaachen-200314001002
Seminar talk: Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model]]>
Seminar talk: Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model]]> Sat, 14 Mar 2020 00:10:02 GMT /slideshow/hierarchical-deterministic-quadrature-methods-for-option-pricing-under-the-rough-bergomi-model/230222805 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model ChihebBenHammouda1 Seminar talk: Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/roughbergomiseminartalkrwthaachen-200314001002-thumbnail.jpg?width=120&height=120&fit=bounds" /> Seminar talk: Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model

Hierarchical Deterministic Quadrature Methods for Option Pricing under the Rough Bergomi Model from Chiheb Ben Hammouda

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0 Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks (SRNs) /slideshow/seminar-talk-multilevel-hybrid-split-step-implicit-tauleap-for-stochastic-reaction-networks-srns/228900696 talksplitstepimplictmlrwthaachen-200222085444
In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics are dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of Stochastic Reaction Networks (SRNs). In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. In this talk, we propose a novel implicit scheme, split-step implicit tau-leap (SSI-TL), to improve numerical stability and provide efficient simulation algorithms for those systems. Furthermore, to estimate statistical quantities related to SRNs, we propose a novel hybrid Multilevel Monte Carlo (MLMC) estimator in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This estimator uses the SSI-TL scheme at levels where the explicit-TL method is not applicable due to numerical stability issues, and then, starting from a certain interface level, it switches to the explicit scheme. We present numerical examples that illustrate the achieved gains of our proposed approach in this context.]]>
In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics are dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of Stochastic Reaction Networks (SRNs). In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. In this talk, we propose a novel implicit scheme, split-step implicit tau-leap (SSI-TL), to improve numerical stability and provide efficient simulation algorithms for those systems. Furthermore, to estimate statistical quantities related to SRNs, we propose a novel hybrid Multilevel Monte Carlo (MLMC) estimator in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This estimator uses the SSI-TL scheme at levels where the explicit-TL method is not applicable due to numerical stability issues, and then, starting from a certain interface level, it switches to the explicit scheme. We present numerical examples that illustrate the achieved gains of our proposed approach in this context.]]> Sat, 22 Feb 2020 08:54:44 GMT /slideshow/seminar-talk-multilevel-hybrid-split-step-implicit-tauleap-for-stochastic-reaction-networks-srns/228900696 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks (SRNs) ChihebBenHammouda1 In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics are dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of Stochastic Reaction Networks (SRNs). In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. In this talk, we propose a novel implicit scheme, split-step implicit tau-leap (SSI-TL), to improve numerical stability and provide efficient simulation algorithms for those systems. Furthermore, to estimate statistical quantities related to SRNs, we propose a novel hybrid Multilevel Monte Carlo (MLMC) estimator in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This estimator uses the SSI-TL scheme at levels where the explicit-TL method is not applicable due to numerical stability issues, and then, starting from a certain interface level, it switches to the explicit scheme. We present numerical examples that illustrate the achieved gains of our proposed approach in this context. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talksplitstepimplictmlrwthaachen-200222085444-thumbnail.jpg?width=120&height=120&fit=bounds" /> In biochemically reactive systems with small copy numbers of one or more reactant molecules, the dynamics are dominated by stochastic effects. To approximate those systems, discrete state-space and stochastic simulation approaches have been shown to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of Stochastic Reaction Networks (SRNs). In systems characterized by having simultaneously fast and slow timescales, existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap (explicit-TL) method, can be very slow. In this talk, we propose a novel implicit scheme, split-step implicit tau-leap (SSI-TL), to improve numerical stability and provide efficient simulation algorithms for those systems. Furthermore, to estimate statistical quantities related to SRNs, we propose a novel hybrid Multilevel Monte Carlo (MLMC) estimator in the spirit of the work by Anderson and Higham (SIAM Multiscal Model. Simul. 10(1), 2012). This estimator uses the SSI-TL scheme at levels where the explicit-TL method is not applicable due to numerical stability issues, and then, starting from a certain interface level, it switches to the explicit scheme. We present numerical examples that illustrate the achieved gains of our proposed approach in this context.

Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks (SRNs) from Chiheb Ben Hammouda

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0 Mcqmc talk /slideshow/mcqmc-talk/155169520 mcqmctalk-190712144530
My talk in the MCQMC Conference 2016, Stanford University. The talk is about Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks.]]>
My talk in the MCQMC Conference 2016, Stanford University. The talk is about Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks.]]> Fri, 12 Jul 2019 14:45:29 GMT /slideshow/mcqmc-talk/155169520 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Mcqmc talk ChihebBenHammouda1 My talk in the MCQMC Conference 2016, Stanford University. The talk is about Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mcqmctalk-190712144530-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk in the MCQMC Conference 2016, Stanford University. The talk is about Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic Reaction Networks.

Mcqmc talk from Chiheb Ben Hammouda

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0 Talk iccf 19_ben_hammouda /slideshow/talk-iccf-19benhammouda/155167731 talkiccf19benhammouda-190712143316
My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.]]>
My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.]]> Fri, 12 Jul 2019 14:33:15 GMT /slideshow/talk-iccf-19benhammouda/155167731 ChihebBenHammouda1@slideshare.net(ChihebBenHammouda1) Talk iccf 19_ben_hammouda ChihebBenHammouda1 My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/talkiccf19benhammouda-190712143316-thumbnail.jpg?width=120&height=120&fit=bounds" /> My talk in the International Conference on Computational Finance 2019 (ICCF2019). The talk is about designing new efficient methods for option pricing under the rough Bergomi model.

Talk iccf 19_ben_hammouda from Chiheb Ben Hammouda

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0 https://cdn.slidesharecdn.com/profile-photo-ChihebBenHammouda1-48x48.jpg?cb=1712480597 Research Scientist at RWTH Aachen University, and Ph.D. in Computational Mathematics, focusing on quantitative finance, uncertainty quantification, computational chemistry/biology. My research area lies in the interface of mathematical modeling, numerical analysis and simulation for i) Developing efficient numerical methods for pricing financial derivatives, and uncertainty quantification in option pricing: based on Monte Carlo (MC), multilevel MC, Quasi-MC, sparse grids, polynomial chaos expansion, Fourier, and deep learning methods. ii) Developing efficient simulation techniques for a robust estimation of statistical quantities for stochastic biological and chemical systems. https://cdn.slidesharecdn.com/ss_thumbnails/presentation-240407090404-967a69e5-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/efficient-fourier-pricing-of-multiasset-options-quasimonte-carlo-domain-transformation-approach/267146790 Efficient Fourier Pric... https://cdn.slidesharecdn.com/ss_thumbnails/leidenvudelftseminarshort-231116125931-568c07b8-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/leidenvudelftseminar-shortpdf/263494313 Leiden_VU_Delft_semina... https://cdn.slidesharecdn.com/ss_thumbnails/presentation-230701084501-6ba8736f-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/presentationpdf-258758027/258758027 Presentation.pdf