Mostafa F., Dillon T., A Neural Network Approach to Option Pricing. In Computational Finance and its Applications III, edited by M. Constantino, M. Larran and C.A. Brebbia, WIT Press, pp. 71- 86, 2008.Mostafa F,Dillon T.A neural network approach to option pricing.WIT Transaction on ...
option pricing; neural networks; statistical inference; model selection Abstract In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power...
optionforecastingpricefuzzyneuralseries W. Pedrycz and S.-M. Chen (Eds.): Granular Computing and Intell. Sys., ISRL 13, pp. 237–248. springerlink © Springer-Verlag Berlin Heidelberg 2011 A Weighted Fuzzy Time Series Based Neural Network Approach to Option Price Forecasting Yungho Leu, Ch...
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(1991), “A neural network approach to forecasting financial distress”, The Journal of Business Forecasting, Vol. 10 No. 4. Cooper, L.A., Holderness, D.K., Sorensen, T.L. and Wood, D.A. (2019), “Robotic process automation in public accounting”, Accounting Horizons, Vol. 33 No...
Here, the corpus of labeled data is used in order to train the weights of a neural network so that once trained it can infer the label of new data. Often, especially in cases where there is a large amount of data, neural networks can achieve better performance than more traditional ...
Do price caps assist monetary authorities to control inflation? Examining the impact of the natural gas price cap on TTF spikes Appendix A. Supplementary data【数据+Stata】 Goutte, S., et al. (2024). Forecasting photovoltaic production with neural networks and weather features Appendix A. ...
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(For the experts: this isn’t quite true, because he still needs a concept of loop momenta. He’s getting the actual integrands from his approach, rather than the dual definition he got from the amplituhedron. But he does still have to integrate one way or another.) ...