Palestra: Gradient Boosting for Inverse Problem
Palestrante: Yuri Saporito (FGV-RIO)

Data: 12/04/2019
Hora: 10h15
Local: IM-UFRJ, CT, sala C-116

Abstract: We proposed a novel non-parametric method to solve inverse problems. The method is based on the Gradient Boosting from the statistical learning literature. The smoothness (using smooth boosts) and robustness of the method generates well-behaved solutions to inverse problems. We will apply the method to the estimation of local volatility functions, a very well-known problem in Quantitative Finance. The method generates well-behaved local volatility surfaces, capable of replicating vanilla option prices and the implied volatility surface.