Pricing and hedging derivative securities with neural networks and a homogeneity hint
René Garcia,Ramazan Gençay +1 more
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TLDR
In this article, a generalized option pricing model with a functional shape similar to the usual Black-Scholes formula is proposed. But instead of setting up a learning network mapping the ratio St/K and the time to maturity directly into the derivative price, the pricing function is broken down into two parts, one controlled by the ratio S/K, the other one by a function of time-to-maturity.About:
This article is published in Journal of Econometrics.The article was published on 2000-01-01 and is currently open access. It has received 211 citations till now. The article focuses on the topics: Black–Scholes model & Rational pricing.read more
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Proceedings Article
Incorporating Second-Order Functional Knowledge for Better Option Pricing
TL;DR: A class of functions similar to multi-layer neural networks but that has those properties of a universal approximator of continuous functions with these and other properties is proposed and applied to the task of modeling the price of call options.
Journal ArticleDOI
Nonparametric option pricing under shape restrictions
TL;DR: In this article, a method to constrain the values of the first and second derivatives of nonparametric locally polynomial estimators was developed to estimate the state price density, or risk-neutral density, implicit in the market prices of options.
Posted Content
Nonparametric Option Pricing under Shape Restrictions
TL;DR: In this paper, a method to constrain the values of the first and second derivatives of nonparametric locally polynomial estimators is proposed to estimate the state price density (SPD), or risk-neutral density, implicit in the market prices of options.
Journal ArticleDOI
Artificial neural networks in business
Michal Tkáč,Robert Verner +1 more
TL;DR: A literature review considering articles on artificial neural networks in business published in last two decades revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks.
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The Importance of the Loss Function in Option Valuation
TL;DR: In this article, the authors emphasize the importance of consistency in the choice of loss functions for parameter estimation and evaluation of option valuation models and illustrate how to apply the so-called Practitioner Black-Scholes model to S&P 500 index options.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
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Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
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Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more