Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models
Expert Systems with Applications: An International Journal
Predicting a distribution of implied volatilities for option pricing
Expert Systems with Applications: An International Journal
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Nonparametric approaches for option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel learning network for option-pricing, which is a nonparametric approach. The main advantages of the proposed method are providing a principled hyper-parameter selection method and the distribution of predicted target value. With these features, we do not need to adjust any parameters at hand for model learning and we can get confidence interval as well as strict predicted target value. Experiments are conducted for the KOSPI200 index daily call options and their results show that the proposed method works excellently to obtain prediction confidence interval and to improve the option-pricing accuracy.