Calculating the price of an option commonly uses numerical methods and can becomputationally heavy. In general, longer computations result in a more precisresult. As such, improving existing models or creating new models have been thefocus in the research field. More recently the focus has instead shifted towardcreating neural networks that can predict the price of a given option directly.This thesis instead studied how the number of time-steps parameter can beoptimized, with regard to precision of the resulting price, and then predict theoptimal number of time-steps for other options. The number of time-stepsparameter determines the computation time of one of the most common models inoption pricing, the Cox-Ross-Rubinstein model (CRR). Two...
A neural network model which processes financial input data is presented to estimate the market pric...
The modern derivatives market has been steadily growing since the development of the first accurate ...
This work deals with the development and validation of an Artificial Neuron Network as a prediction ...
Calculating the price of an option commonly uses numerical methods and can becomputationally heavy. ...
This thesis examines the application of neural networks in the context of option pricing. Throughout...
A neural network model that processes financial input data is developed to estimate the market price...
The task of pricing options is one with many different solutions, and overtime more complicated mode...
ABSTRACT This dissertation comprising part of a Master Course in Computational Finance investigates...
Options are an important financial derivative for the investors to control their investment risks in...
Machine learning techniques have revolutionized the field of financial engineering by providing accu...
In the modern day, Artificial Neural Networks (ANNs) have been recently used and tested to calculate...
Options are important financial derivatives that allow investors to control their investment risks i...
Nonparametric approaches of option pricing have recently emerged as alternative approaches that comp...
In this paper the pricing performance of the artificial neural network is compared to the Black-Scho...
With the emergence of more complex option pricing models, the demand for fast and accurate numerical...
A neural network model which processes financial input data is presented to estimate the market pric...
The modern derivatives market has been steadily growing since the development of the first accurate ...
This work deals with the development and validation of an Artificial Neuron Network as a prediction ...
Calculating the price of an option commonly uses numerical methods and can becomputationally heavy. ...
This thesis examines the application of neural networks in the context of option pricing. Throughout...
A neural network model that processes financial input data is developed to estimate the market price...
The task of pricing options is one with many different solutions, and overtime more complicated mode...
ABSTRACT This dissertation comprising part of a Master Course in Computational Finance investigates...
Options are an important financial derivative for the investors to control their investment risks in...
Machine learning techniques have revolutionized the field of financial engineering by providing accu...
In the modern day, Artificial Neural Networks (ANNs) have been recently used and tested to calculate...
Options are important financial derivatives that allow investors to control their investment risks i...
Nonparametric approaches of option pricing have recently emerged as alternative approaches that comp...
In this paper the pricing performance of the artificial neural network is compared to the Black-Scho...
With the emergence of more complex option pricing models, the demand for fast and accurate numerical...
A neural network model which processes financial input data is presented to estimate the market pric...
The modern derivatives market has been steadily growing since the development of the first accurate ...
This work deals with the development and validation of an Artificial Neuron Network as a prediction ...