Statistically Principled Application of Computational Intelligence Techniques for Finance

  • Jerome V. Healy
Part of the Springer Optimization and Its Applications book series (SOIA, volume 70)


Computational techniques forregression have been widely applied to asset pricing, return forecasting, volatility forecasting, credit risk assessment, and value at risk estimation, among other tasks. Determining probabilistic bounds on results is essential in these contexts. This chapter provides an exposition of methods for estimating confidence and prediction intervals on outputs, forcomputational intelligence tools used for data modelling. The exposition focuses on neural nets as exemplars. However, the techniques and theory outlined apply to any equivalent computational intelligence technique used for regression. A recently developed robust method of computingprediction intervals, appropriate to any such regression technique of sufficient generality, is described.


Option Price Noise Variance Prediction Interval Hide Layer Node True Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.UEL Royal Docks Business SchoolUniversity of East LondonLondonUK

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