Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A novel learning network for option pricing with confidence interval information
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
IEEE Transactions on Neural Networks
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
Predicting a distribution of implied volatilities for option pricing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
Transductive Bayesian regression via manifold learning of prior data structure
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Gaussian process (GP) model is a Bayesian kernel-based learning machine. In this paper, we propose a GP model with a various mixed kernel for pricing and hedging ELWs (equity linked warrants) traded at KRX with predictive distribution. We experiment with daily market data relevant to KOSPI200 call ELWs from March 2006 to July 2006, comparing the performance of the GP model with those of various neural network (NN) models to show its effectiveness. The applied NN models contain early stopping, regularized NN, and bagging. The proposed GP model shows that its forecast capability outperforms those of the three NN models in terms of both pricing and hedging errors, thereby generating consistent results.