U. Anders, O. Korn, and C. Schmitt, "Improving the Pricing of Options: A Neural Network Approach," Journal of Forecasting, vol. 17, pp. 369-- 388, 1998. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Pareto Evolutionary Neural Networks - Fieldsend, Singh (2003) (Correct)
....final set of archived Pareto optimal members, F T, should provide an estimate of the trade off of the risk return defined by the generating process and trading strategy. Financial forecasting (modelling the generating process of a financial time series, or process) is a popular application of NNs [1, 3, 18, 20, 23, 24, 28, 34, 39, 40, 43, 44, 47, 54]. However, in a number of studies misleading claims are made (or inferred) with regards to the actually efficiency of the models presented. Typically the accuracy of a model is described for some data set (usually in terms of Euclidean error) and an estimate of the profit generated 13 by using ....
U. Anders, O. Korn, and C. Schmitt. Improving the Pricing of Options: A Neural Network Approach. Journal of Forecasting, 17:369-388, 1998.
Risk-neutral Density Extraction from Option Prices.. - Schittenkopf, Dorffner (2000) (Correct)
....GARCH model [8] or the time continuous stochastic volatility models (see, for instance, 10] have been derived. As an alternative approach, neural networks have been successfully applied to estimate pricing formulae of financial derivatives, also in combination with the BlackScholes model [3, 9, 13]. The parameters of an option pricing model (such as the volatility in the BlackScholes model) can be estimated from historical time series or from option prices observed at financial markets. Especially the latter approach has attracted increasing interest over the last decade (see, for ....
....log normal density of stock prices whereas Corrado and Su [7] use a Gram Charlier series expansion of the normal density of log returns. Another approach is to model the pricing function of a European call option, e.g. by using a nonparametric estimator [2] or a neural network based pricing model [3, 9, 13] and to differentiate twice with respect to the strike price. Finally, the risk neutral density can also be estimated directly using nonparametric methods [14] or a mixture of Gaussians approach [21, 22] This paper introduces a more general framework for risk neutral density estimation from ....
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Anders, U., O. Korn, and C. Schmitt (1998) "Improving the pricing of options: a neural network approach", Journal of Forecasting 17, 369-388.
Pareto Evolutionary Neural Networks - Jonathan Fieldsend Member (Correct)
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U. Anders, O. Korn, and C. Schmitt, "Improving the Pricing of Options: A Neural Network Approach," Journal of Forecasting, vol. 17, pp. 369-- 388, 1998.
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