Second order discretization of backward SDEs and simulation with the cubature method

By D. Crisan, K. Manolarakis. 2014

54 citations recorded by the source corpus.

Subfields: Demography, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b4c389ede1ab7a952b85529eed1414689198edf3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Arnulf Jentzen
2. Christian Beck
3. W. E
4. Martin Hutzenthaler
5. Philippe von Wurstemberger
6. Xuerong Mao
7. Sotirios Sabanis

In Finance

1. Arnulf Jentzen
2. Christian Beck
3. Martin Hutzenthaler
4. W. E
5. Philippe von Wurstemberger
6. Xuerong Mao
7. Sotirios Sabanis
8. Peter E. Kloeden

In Statistics and Probability

1. Christian Beck
2. W. E
3. Martin Hutzenthaler
4. Arnulf Jentzen
5. Philippe von Wurstemberger
6. Peter E. Kloeden

Citations in the corpus

• Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks (2019, methodology): “, [2, 6, 7, 10, 12, 13, 17, 18, 19, 20, 25, 26, 27, 28, 38, 39, 31, 32, 40, 41, 42, 43, 44, 45, 46, 57, 69, 70, 71, 72, 73, 74, 78, 79, 82, 83, 84, 85, 89, 90, 91, 96, 102, 103, 104] for probabilistic approximation methods for PDEs using discretizations of the associated backward stochastic differen”
• Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations (2017, methodology): “tations based on extensions of the classical Feynman-Kac formula (cf., e.g., [34, 58, 84]). We refer, e.g., to [23, 30, 65, 69, 95, 103, 104, 106] for deterministic approximation methods for PDEs, to [3, 6, 7, 13, 18, 19, 20, 21, 25, 26, 27, 28, 31, 32, 43, 44, 45, 46, 47, 48, 56, 64, 71, 72, 73, 74”