Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Valuation of an American option with Monte Carlo methods is one of the most important and difficult problems in pricing, since it involves the determination of optimal exercise timing in the sense that the option can be exercised at any time prior to its own maturity. Regression approaches have been widely used to price an American-style option approximately with Monte Carlo simulation. However, the conventional regression methods are very sensitive in the kind and the number of their basis functions, thereby affecting prediction accuracy. In this paper, we propose a novel kernel-based Monte Carlo simulation algorithm to overcome such shortcomings of the regression approaches and conduct a simulation on some American options with promising results on its pricing accuracy.