Improving Bayesian Regularization of ANN via Pre-training with Early-Stopping
Neural Processing Letters
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models
Expert Systems with Applications: An International Journal
Knowledge discovery in corporate events by neural network rule extraction
Applied Intelligence
Financial Prediction with Neuro-fuzzy Systems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Option valuation based on the neural regression model
Expert Systems with Applications: An International Journal
Bagging for Gaussian process regression
Neurocomputing
Option pricing with modular neural networks
IEEE Transactions on Neural Networks
A Multi-agent System to Assist with Real Estate Appraisals Using Bagging Ensembles
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Predicting a distribution of implied volatilities for option pricing
Expert Systems with Applications: An International Journal
A multi-agent system to assist with property valuation using heterogeneous ensembles of fuzzy models
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Range estimation of construction costs using neural networks with bootstrap prediction intervals
Expert Systems with Applications: An International Journal
Neuro-genetic system for stock index prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Early stopping criteria to counteract overfitting in genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
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
Pricing And Hedging Short Sterling Options Using Neural Networks
International Journal of Intelligent Systems in Accounting and Finance Management
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Hi-index | 0.01 |
We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available