Rate of convergence of an empirical regression method for solving generalized backward stochastic differential equations

By Jean-Philippe Lemor, Emmanuel Gobet, Xavier Warin. 2006

191 citations recorded by the source corpus.

Subfields: Demography, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/be7577a1662bd36c70dcb12c7448a256b4022c9d

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. D. Crisan
2. Arnulf Jentzen
3. K. Manolarakis
4. Emmanuel Gobet
5. Christian Bender
6. Martin Hutzenthaler
7. Philippe von Wurstemberger
8. Bruno Bouchard

In Finance

1. Arnulf Jentzen
2. D. Crisan
3. K. Manolarakis
4. Martin Hutzenthaler
5. Philippe von Wurstemberger
6. Christian Bender
7. Emmanuel Gobet
8. Jessica Steiner

In Statistics and Probability

1. D. Crisan
2. K. Manolarakis
3. Christian Bender
4. Arnulf Jentzen
5. Nizar Touzi
6. Jessica Steiner
7. Martin Hutzenthaler
8. Philippe von Wurstemberger

Citations in the corpus

• On the Monte Carlo simulation of BSDEs: An improvement on the Malliavin weights (2010, methodology): “In contrast with the above literature, we concenrate on approximations of solutions of backward stochastic differential equations based on the Monte Carlo simulation of the underlying Brownian motion, thus continuing the studies of Bouchard and Touzi [6], Zhang [18] and Gobet, Lemor and Warin [9].”
• 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”
• Error Criteria for Numerical Solutions of Backward SDEs (2010, methodology): “Possible choices for these simulation based procedures are least-squares Monte Carlo [5, 18, 23], quantization [2, 14], Malliavin Monte Carlo [10], or non-parametric regression [10, 12].”
• Error expansion for the discretization of backward stochastic differential equations (2006, methodology): “We refer to Bouchard and Touzi [2] for Malliavin calculus techniques, or to Gobet et al. [6], Lemor et al. [ 13 ] for empirical regression methods.”
• Monte-Carlo valuation of American options: facts and new algorithms to improve existing methods (2012, methodology): “Finally, this approach requires a non-degeneracy assumpti on on σ , while the regression based method may be applied to large class of Mark ov processes, see [29].”
• A PRIMAL–DUAL ALGORITHM FOR BSDES (2013, methodology): “We first generate approximations ỹLGW, q̃ LGW, z̃LGW by the least-squares Monte Carlo algorithm of Lemor et al. (2006).”