High-Dimensional Regression on Sparse Grids Applied to Pricing Moving Window Asian Options
Author(s)
Stefan Dirnstorfer, Andreas J. Grau, Rudi ZagstABSTRACT
The pricing of moving window Asian option with an early exercise feature is considered a challenging problem in option pricing. The computational challenge lies in the unknown optimal exercise strategy and in the high dimensionality required for approximating the early exercise boundary. We use sparse grid basis functions in the Least Squares Monte Carlo approach to solve this “curse of dimensionality” problem. The resulting algorithm provides a general and convergent method for pricing moving window Asian options. The sparse grid technique presented in this paper can be generalized to pricing other high-dimensional, early-exercisable derivatives.KEYWORDS
Sparse Grid; Regression; Least-Squares Monte Carlo; Moving Window Asian OptionLink
S. Dirnstorfer, A. Grau and R. Zagst, "High-Dimensional Regression on Sparse Grids Applied to Pricing Moving Window Asian Options," Open Journal of Statistics, Vol. 3 No. 6, 2013, pp. 427-440. doi: 10.4236/ojs.2013.36051.
1 comment:
SAS assignments were very challenging for me because working with large datasets and unpredictable errors made the process stressful. After choosing SAS Assignment Writing Service I finally understood how to manage data-step programming and organise my work effectively. The experts explained difficult concepts clearly and helped me build confidence in my skills. Rapid Assignment Help delivered a well-structured and accurate assignment that matched all my academic requirements. Their guidance truly improved my overall performance in SAS.
Post a Comment